Subjects -> MEDICAL SCIENCES (Total: 8442 journals)
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MEDICAL SCIENCES (2305 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 3562 Journals sorted alphabetically
16 de Abril     Open Access   (Followers: 2)
3D Printing in Medicine     Open Access   (Followers: 4)
4 open     Open Access  
AADE in Practice     Hybrid Journal   (Followers: 6)
ABCS Health Sciences     Open Access   (Followers: 8)
Abia State University Medical Students' Association Journal     Full-text available via subscription   (Followers: 2)
AboutOpen     Open Access  
ACIMED     Open Access   (Followers: 1)
ACS Medicinal Chemistry Letters     Hybrid Journal   (Followers: 48)
Acta Bio Medica     Full-text available via subscription   (Followers: 2)
Acta Bioethica     Open Access  
Acta Bioquimica Clinica Latinoamericana     Open Access   (Followers: 1)
Acta Científica Estudiantil     Open Access  
Acta Facultatis Medicae Naissensis     Open Access   (Followers: 1)
Acta Herediana     Open Access  
Acta Informatica Medica     Open Access  
Acta Medica (Hradec Králové)     Open Access  
Acta Medica Bulgarica     Open Access  
Acta Medica Colombiana     Open Access   (Followers: 1)
Acta Médica Costarricense     Open Access   (Followers: 2)
Acta Medica Indonesiana     Open Access  
Acta Medica International     Open Access  
Acta medica Lituanica     Open Access  
Acta Medica Marisiensis     Open Access  
Acta Medica Martiniana     Open Access  
Acta Medica Nagasakiensia     Open Access   (Followers: 1)
Acta Medica Peruana     Open Access   (Followers: 2)
Acta Médica Portuguesa     Open Access  
Acta Medica Saliniana     Open Access  
Acta Scientiarum. Health Sciences     Open Access   (Followers: 2)
Acupuncture & Electro-Therapeutics Research     Full-text available via subscription   (Followers: 6)
Acupuncture and Natural Medicine     Open Access  
Addiction Science & Clinical Practice     Open Access   (Followers: 8)
Addictive Behaviors Reports     Open Access   (Followers: 9)
Adıyaman Üniversitesi Sağlık Bilimleri Dergisi / Health Sciences Journal of Adıyaman University     Open Access   (Followers: 1)
Adnan Menderes Üniversitesi Sağlık Bilimleri Fakültesi Dergisi     Open Access   (Followers: 1)
Advanced Biomedical Research     Open Access  
Advanced Health Care Technologies     Open Access   (Followers: 10)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 8)
Advanced Therapeutics     Hybrid Journal   (Followers: 1)
Advances in Bioscience and Clinical Medicine     Open Access   (Followers: 8)
Advances in Cell and Gene Therapy     Hybrid Journal   (Followers: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 27)
Advances in Clinical Radiology     Full-text available via subscription   (Followers: 1)
Advances in Life Course Research     Hybrid Journal   (Followers: 10)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Medical Education and Practice     Open Access   (Followers: 32)
Advances in Medical Ethics     Open Access   (Followers: 1)
Advances in Medical Research     Open Access   (Followers: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 9)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 6)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 5)
Advances in Molecular Oncology     Open Access   (Followers: 2)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7)
Advances in Parkinson's Disease     Open Access  
Advances in Phytomedicine     Full-text available via subscription  
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20)
Advances in Regenerative Medicine     Open Access   (Followers: 3)
Advances in Skeletal Muscle Function Assessment     Open Access  
Advances in Therapy     Hybrid Journal   (Followers: 5)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 6)
Advances in Wound Care     Hybrid Journal   (Followers: 12)
Aerospace Medicine and Human Performance     Full-text available via subscription   (Followers: 13)
African Health Sciences     Open Access   (Followers: 4)
African Journal of Biomedical Research     Open Access   (Followers: 1)
African Journal of Clinical and Experimental Microbiology     Open Access   (Followers: 4)
African Journal of Laboratory Medicine     Open Access   (Followers: 2)
African Journal of Medical and Health Sciences     Open Access   (Followers: 3)
African Journal of Thoracic and Critical Care Medicine     Open Access  
African Journal of Trauma     Open Access   (Followers: 1)
Afrimedic Journal     Open Access   (Followers: 2)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
AIDS Research and Human Retroviruses     Hybrid Journal   (Followers: 9)
AJOB Empirical Bioethics     Hybrid Journal   (Followers: 3)
AJSP: Reviews & Reports     Hybrid Journal  
Aktuelle Ernährungsmedizin     Hybrid Journal   (Followers: 4)
Al-Azhar Assiut Medical Journal     Open Access   (Followers: 2)
Al-Qadisiah Medical Journal     Open Access   (Followers: 1)
ALERTA : Revista Científica del Instituto Nacional de Salud     Open Access  
Alexandria Journal of Medicine     Open Access   (Followers: 1)
Allgemeine Homöopathische Zeitung     Hybrid Journal   (Followers: 3)
Alpha Omegan     Full-text available via subscription  
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 2)
Althea Medical Journal     Open Access   (Followers: 2)
American Journal of Biomedical Engineering     Open Access   (Followers: 15)
American Journal of Biomedical Research     Open Access   (Followers: 2)
American Journal of Biomedicine     Full-text available via subscription   (Followers: 7)
American Journal of Chinese Medicine, The     Hybrid Journal   (Followers: 4)
American Journal of Clinical Medicine Research     Open Access   (Followers: 8)
American Journal of Family Therapy     Hybrid Journal   (Followers: 10)
American Journal of Law & Medicine     Full-text available via subscription   (Followers: 11)
American Journal of Lifestyle Medicine     Hybrid Journal   (Followers: 6)
American Journal of Managed Care     Full-text available via subscription   (Followers: 12)
American Journal of Medical Case Reports     Open Access   (Followers: 1)
American Journal of Medical Sciences and Medicine     Open Access   (Followers: 4)
American Journal of Medicine     Hybrid Journal   (Followers: 51)
American Journal of Medicine and Medical Sciences     Open Access   (Followers: 1)
American Journal of Medicine Studies     Open Access   (Followers: 3)
American Journal of Medicine Supplements     Full-text available via subscription   (Followers: 3)
American Journal of the Medical Sciences     Hybrid Journal   (Followers: 12)
American Journal on Addictions     Hybrid Journal   (Followers: 10)
American medical news     Free   (Followers: 3)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 6)
Amyloid: The Journal of Protein Folding Disorders     Hybrid Journal   (Followers: 5)
Anales de la Facultad de Medicina     Open Access  
Anales de la Facultad de Medicina, Universidad de la República, Uruguay     Open Access  
Anales del Sistema Sanitario de Navarra     Open Access   (Followers: 1)
Analgesia & Resuscitation : Current Research     Hybrid Journal   (Followers: 6)
Anatolian Clinic the Journal of Medical Sciences     Open Access  
Anatomica Medical Journal     Open Access  
Anatomical Science International     Hybrid Journal   (Followers: 3)
Anatomical Sciences Education     Hybrid Journal   (Followers: 2)
Anatomy     Open Access   (Followers: 3)
Anatomy Research International     Open Access   (Followers: 4)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3)
Ankara Medical Journal     Open Access   (Followers: 2)
Ankara Üniversitesi Tıp Fakültesi Mecmuası     Open Access  
Annales de Pathologie     Full-text available via subscription  
Annales des Sciences de la Santé     Open Access  
Annales françaises d'Oto-rhino-laryngologie et de Pathologie Cervico-faciale     Full-text available via subscription   (Followers: 3)
Annals of African Medicine     Open Access   (Followers: 2)
Annals of Anatomy - Anatomischer Anzeiger     Hybrid Journal   (Followers: 3)
Annals of Bioanthropology     Open Access   (Followers: 5)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 19)
Annals of Biomedical Sciences     Full-text available via subscription   (Followers: 4)
Annals of Clinical Hypertension     Open Access  
Annals of Clinical Microbiology and Antimicrobials     Open Access   (Followers: 14)
Annals of Family Medicine     Open Access   (Followers: 15)
Annals of Health Research     Open Access   (Followers: 1)
Annals of Ibadan Postgraduate Medicine     Open Access  
Annals of Medical and Health Sciences Research     Open Access   (Followers: 7)
Annals of Medicine     Hybrid Journal   (Followers: 12)
Annals of Medicine and Surgery     Open Access   (Followers: 7)
Annals of Microbiology     Hybrid Journal   (Followers: 13)
Annals of Nigerian Medicine     Open Access   (Followers: 1)
Annals of Rehabilitation Medicine     Open Access  
Annals of Saudi Medicine     Open Access  
Annals of the College of Medicine, Mosul     Open Access   (Followers: 1)
Annals of the New York Academy of Sciences     Hybrid Journal   (Followers: 5)
Annals of The Royal College of Surgeons of England     Full-text available via subscription   (Followers: 3)
Annual Reports in Medicinal Chemistry     Full-text available via subscription   (Followers: 7)
Annual Reports on NMR Spectroscopy     Full-text available via subscription   (Followers: 5)
Annual Review of Medicine     Full-text available via subscription   (Followers: 17)
Anthropological Review     Open Access   (Followers: 24)
Anthropologie et santé     Open Access   (Followers: 5)
Antibiotics     Open Access   (Followers: 9)
Antibodies     Open Access   (Followers: 2)
Antibody Technology Journal     Open Access   (Followers: 1)
Antibody Therapeutics     Open Access  
Anuradhapura Medical Journal     Open Access  
Anwer Khan Modern Medical College Journal     Open Access   (Followers: 2)
Apmis     Hybrid Journal   (Followers: 1)
Apparence(s)     Open Access   (Followers: 1)
Applied Clinical Informatics     Hybrid Journal   (Followers: 4)
Applied Clinical Research, Clinical Trials and Regulatory Affairs     Hybrid Journal   (Followers: 2)
Applied Medical Informatics     Open Access   (Followers: 13)
Arab Journal of Nephrology and Transplantation     Open Access   (Followers: 1)
Archive of Clinical Medicine     Open Access   (Followers: 1)
Archive of Community Health     Open Access   (Followers: 1)
Archives Medical Review Journal / Arşiv Kaynak Tarama Dergisi     Open Access  
Archives of Asthma, Allergy and Immunology     Open Access  
Archives of Medical and Biomedical Research     Open Access   (Followers: 3)
Archives of Medical Laboratory Sciences     Open Access   (Followers: 1)
Archives of Medicine and Health Sciences     Open Access   (Followers: 4)
Archives of Medicine and Surgery     Open Access  
Archives of Trauma Research     Open Access   (Followers: 4)
Archivos de Medicina (Manizales)     Open Access  
ArgoSpine News & Journal     Hybrid Journal  
Arquivos Brasileiros de Oftalmologia     Open Access   (Followers: 1)
Arquivos de Ciências da Saúde     Open Access  
Arquivos de Medicina     Open Access  
Ars Medica : Revista de Ciencias Médicas     Open Access  
ARS Medica Tomitana     Open Access   (Followers: 1)
Art Therapy: Journal of the American Art Therapy Association     Hybrid Journal   (Followers: 17)
Arterial Hypertension     Open Access   (Followers: 1)
Artificial Intelligence in Medicine     Hybrid Journal   (Followers: 18)
Artificial Organs     Hybrid Journal   (Followers: 1)
ASHA Leader     Open Access   (Followers: 3)
Asia Pacific Family Medicine Journal     Open Access   (Followers: 2)
Asia Pacific Journal of Clinical Nutrition     Full-text available via subscription   (Followers: 13)
Asia Pacific Journal of Clinical Trials : Nervous System Diseases     Open Access  
Asian Bioethics Review     Full-text available via subscription   (Followers: 3)
Asian Biomedicine     Open Access   (Followers: 2)
Asian Journal of Cell Biology     Open Access   (Followers: 6)
Asian Journal of Health     Open Access   (Followers: 3)
Asian Journal of Medical and Biological Research     Open Access   (Followers: 5)
Asian Journal of Medical and Pharmaceutical Researches     Open Access   (Followers: 2)
Asian Journal of Medical Sciences     Open Access   (Followers: 2)
Asian Journal of Medicine and Health     Open Access   (Followers: 1)
Asian Journal of Research in Medical and Pharmaceutical Sciences     Open Access   (Followers: 1)
Asian Journal of Scientific Research     Open Access   (Followers: 3)
Asian Journal of Transfusion Science     Open Access   (Followers: 1)
Asian Medicine     Hybrid Journal   (Followers: 5)
Asian Pacific Journal of Cancer Prevention     Open Access  
ASPIRATOR : Journal of Vector-borne Disease Studies     Open Access  
Astrocyte     Open Access  

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Artificial Intelligence in Medicine
Journal Prestige (SJR): 0.766
Citation Impact (citeScore): 3
Number of Followers: 18  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0933-3657 - ISSN (Online) 1873-2860
Published by Elsevier Homepage  [3206 journals]
  • Efficient Treatment of Outliers and Class Imbalance for Diabetes
    • Abstract: Publication date: Available online 10 February 2020Source: Artificial Intelligence in MedicineAuthor(s): Nonso Nnamoko, Ioannis KorkontzelosLearning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedicated to tackle this problem, data preprocessing solutions are known to be efficient and easy to implement. In this paper, we propose a selective data preprocessing approach that embeds knowledge of the outlier instances into artificially generated subset to achieve an even distribution. The synthetic minority oversampling technique (smote) was used to balance the training data by introducing artificial minority instances. However, this was not before the outliers were identified and oversampled (irrespective of class). The aim is to balance the training dataset while controlling the effect of outliers. The experiments prove that such selective oversampling empowers smote, ultimately leading to improved classification performance.Graphical abstractGraphical abstract for this article
  • Real-world data medical knowledge graph: construction and applications
    • Abstract: Publication date: Available online 6 February 2020Source: Artificial Intelligence in MedicineAuthor(s): Linfeng Li, Peng Wang, Jun Yan, Yao Wang, Simin Li, Jinpeng Jiang, Zhe Sun, Buzhou Tang, Tsung-Hui Chang, Shenghui Wang, Yuting LiuAbstractObjectiveMedical knowledge graph (KG) is attracting attention from both academic and healthcare industry due to its power in intelligent healthcare applications. In this paper, we introduce a systematic approach to build medical KG from electronic medical records (EMRs) with evaluation by both technical experiments and end to end application examples.Materials and MethodsThe original data set contains 16,217,270 de-identified clinical visit data of 3,767,198 patients. The KG construction procedure includes 8 steps, which are data preparation, entity recognition, entity normalization, relation extraction, property calculation, graph cleaning, related-entity ranking, and graph embedding respectively. We propose a novel quadruplet structure to represent medical knowledge instead of the classical triplet in KG. A novel related-entity ranking function considering probability, specificity and reliability (PSR) is proposed. Besides, probabilistic translation on hyperplanes (PrTransH) algorithm is used to learn graph embedding for the generated KG.ResultsA medical KG with 9 entity types including disease, symptom, etc. was established, which contains 22,508 entities and 579,094 quadruplets. Compared with term frequency - inverse document frequency (TF/IDF) method, the normalized discounted cumulative gain (NDCG@10) increased from 0.799 to 0.906 with the proposed ranking function. The embedding representation for all entities and relations were learned, which are proven to be effective using disease clustering.ConclusionThe established systematic procedure can efficiently construct a high-quality medical KG from large-scale EMRs. The proposed ranking function PSR achieves the best performance under all relations, and the disease clustering result validates the efficacy of the learned embedding vector as entity’s semantic representation. Moreover, the obtained KG finds many successful applications due to its statistics-based quadruplet.where Ncomin is a minimum co-occurrence number and R is the basic reliability value. The reliability value can measure how reliable is the relationship between Si and Oij. The reason for the definition is the higher value of Nco(Si, Oij), the relationship is more reliable. However, the reliability values of the two relationships should not have a big difference if both of their co-occurrence numbers are very big. In our study, we finally set Ncomin = 10 and R = 1 after some experiments. For instance, if co-occurrence numbers of three relationships are 1, 100 and 10000, their reliability values are 1, 2.96 and 5 respectively.
  • A multicenter random forest model for effective prognosis prediction in
           collaborative clinical research network
    • Abstract: Publication date: Available online 5 February 2020Source: Artificial Intelligence in MedicineAuthor(s): Jin Li, Yu Tian, Yan Zhu, Tianshu Zhou, Jun Li, Kefeng Ding, Jingsong LiAbstractBackgroundThe accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. However, sharing sensitive biomedical data for research involves complicated issues. Machine learning models such as random forests (RF), though they are commonly used and achieve good performances for prognostic prediction, usually suffer worse performance under multicenter privacy-preserving data mining scenarios compared to a centrally trained version.Methods and materialsIn this study, a multicenter random forest prognosis prediction model is proposed that enables federated clinical data mining from horizontally partitioned datasets. By using a novel data enhancement approach based on a differentially private generative adversarial network customized to clinical prognosis data, the proposed model is able to provide a multicenter RF model with performances on par with—or even better than—centrally trained RF but without the need to aggregate the raw data. Moreover, our model also incorporates an importance ranking step designed for feature selection without sharing patient-level information.ResultThe proposed model was evaluated on colorectal cancer datasets from the US and China. Two groups of datasets with different levels of heterogeneity within the collaborative research network were selected. First, we compare the performance of the distributed random forest model under different privacy parameters with different percentages of enhancement datasets and validate the effectiveness and plausibility of our approach. Then, we compare the discrimination and calibration ability of the proposed multicenter random forest with a centrally trained random forest model and other tree-based classifiers as well as some commonly used machine learning methods. The results show that the proposed model can provide better prediction performance in terms of discrimination and calibration ability than the centrally trained RF model or the other candidate models while following the privacy-preserving rules in both groups. Additionally, good discrimination and calibration ability are shown on the simplified model based on the feature importance ranking in the proposed approach.ConclusionThe proposed random forest model exhibits ideal prediction capability using multicenter clinical data and overcomes the performance limitation arising from privacy guarantees. It can also provide feature importance ranking across institutions without pooling the data at a central site. This study offers a practical solution for building a prognosis prediction model in the collaborative clinical research network and solves practical issues in real-world applications of medical artificial intelligence.
  • Automatic computation of mandibular indices in dental panoramic
           radiographs for early osteoporosis detection
    • Abstract: Publication date: Available online 5 February 2020Source: Artificial Intelligence in MedicineAuthor(s): Ignacio Aliaga, Vicente Vera, María Vera, Enrique García, María Pedrera, Gonzalo PajaresAbstractAIMA new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular Index, Mandibular Ratio, Mandibular Cortical Index) in order to detect the thinning and deterioration of the mandibular bone. Data can be stored for posterior massive analysis.METHODSPanoramic radiographies are intrinsically complex, including: artificial structures, unclear limits in bony structures, jawbones with irregular curvatures and intensity levels, irregular shapes and borders of the mental foramen, irregular teeth alignments or missing dental pieces. An intelligent sequence of linked imaging segmentation processes is proposed to cope with the above situations towards the design of the automatic segmentation, making the following contributions: (i) Fuzzy K-means classification for identifying artificial structures; (ii) adjust a tangent line to the lower border of the lower jawbone (lower cortex), based on texture analysis, grey scale dilation, binarization and labelling; (iii) identification of the mental foramen region and its centre, based on multi-thresholding, binarization, morphological operations and labelling; (iv) tracing a perpendicular line to the tangent passing through the centre of the mental foramen region and two parallel lines to the tangent, passing through borders on the mental foramen intersected by the perpendicular; (v) following the perpendicular line, a sweep is made moving up the tangent for detecting accumulation of binary points after applying adaptive filtering; (vi) detection of the lower mandible alveolar crest line based on the identification of inter-teeth gaps by saliency and interest points feature description.RESULTSThe performance of the proposed approach was quantitatively compared against the criteria of expert dentists, verifying also its validity with statistical studies based on the analysis of deterioration of bone structures with different levels of osteoporosis. All indices are computed inside two regions of interest, which tolerate flexibility in sizes and locations, making this process robust enough.CONCLUSIONSThe proposed approach provides an automatic procedure able to process with efficiency and reliability panoramic X-Ray images for early osteoporosis detection.
  • Random Forest Enhancement using Improved Artificial Fish Swarm for the
           Medial Knee Contact Force Prediction
    • Abstract: Publication date: Available online 3 February 2020Source: Artificial Intelligence in MedicineAuthor(s): Yean Zhu, Weiyi XU, Guoliang Luo, Haolun Wang, Jingjing Yang, Wei LuAbstractKnee Contact Force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the artificial fish swarm and the random forest algorithm. First, we train a random forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the random forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.
  • An Incremental Explanation of Inference in Bayesian Networks for
           Increasing Model Trustworthiness and Supporting Clinical Decision Making
    • Abstract: Publication date: Available online 31 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Evangelia Kyrimi, Somayyeh Mossadegh, Nigel Tai, William MarshAbstractVarious AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to ‘hybrid’ BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.
  • Mixed-Integer Optimization Approach to Learning Association Rules for
           Unplanned ICU Transfer
    • Abstract: Publication date: Available online 30 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Chun-An Chou, Qingtao Cao, Shao-Jen Weng, Che-Hung TsaiAbstractAfter admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Most existing prediction studies were based on univariate analysis or multiple logistic regression to provide one-size-fit-all results. However, patient condition varying from case to case may not be accurately examined by such a simplistic judgment. In this study, we present a new decision tool using a mathematical optimization approach aiming to automatically discover rules associating diagnostic features with high-risk outcome (i.e., unplanned transfers) in different deterioration scenarios. We consider four mutually exclusive patient subgroups based on the principal reasons of ED visits: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases at a suburban teaching hospital. The analysis results demonstrate significant rules associated with unplanned transfer outcome for each subgroups and also show comparable prediction accuracy (>70%) compared to state-of-the-art machine learning methods while providing easy-to-interpret symptom-outcome information.
  • Retraction notice to “Diagnosis Labeling with Disease-Specific
           Characteristics Mining” [Artif. Intell. Med. 90 (2018) 25–33]
    • Abstract: Publication date: Available online 27 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Jun Guo, Xuan Yuan, Xia Zheng, Xu Pengfei, Yun Xiao, Baoying Liu
  • Batch Mode Active Learning on the Riemannian Manifold for Automated
           Scoring of Nuclear Pleomorphism in Breast Cancer
    • Abstract: Publication date: Available online 25 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Asha Das, Madhu S. Nair, David Peter S.AbstractBreast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely prognosis and felicitous treatment planning, by utilizing the computer aided detection and diagnosis techniques. With the advent of whole slide image (WSI) scanners for digitizing the histopathological tissue samples, there is a drastic increase in the availability of digital histopathological images. However, these samples are often unlabeled and hence they need labeling to be done through manual annotations by domain experts and experienced pathologists. But this annotation process required for acquiring high quality large labeled training set for nuclear atypia scoring is a tedious, expensive and time consuming job. Active learning techniques have achieved widespread acceptance in reducing this human effort in annotating the data samples. In this paper, we explore the possibilities of active learning on nuclear pleomorphism scoring over a non-Euclidean framework, the Riemannian manifold. Active learning technique adopted for the cancer grading is in the batch-mode framework, that adaptively identifies the apt batch size along with the batch of instances to be queried, following a submodular optimization framework. Samples for annotation are selected considering the diversity and redundancy between the pair of samples, based on the kernelized Riemannian distance measures such as log-Euclidean metrics and the two Bregman divergences - Stein and Jeffrey divergences. Results of the adaptive Batch Mode Active Learning on the Riemannian metric show a superior performance when compared with the state-of-the-art techniques for breast cancer nuclear pleomorphism scoring, as it makes use of the information from the unlabeled samples.
  • Classification of glomerular hypercellularity using convolutional features
           and support vector machine
    • Abstract: Publication date: Available online 25 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Paulo Chagas, Luiz Souza, Ikaro Araújo, Nayze Aldeman, Angelo Duarte, Michele Angelo, Washington LC dos-Santos, Luciano OliveiraAbstractGlomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results on FIOCRUZ data set in a binary classification (lesion or normal). Additionally, classification of hypercellularity sub-lesions was also evaluated, considering mesangial, endocapilar and both lesions, reaching an average accuracy of 82%. Either in binary task or in the multi-classification one, our proposed method outperformed Xception, ResNet50 and InceptionV3 networks, as well as a traditional handcrafted-based method. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.
  • Quantitative knowledge presentation models of Traditional Chinese Medicine
           (TCM): A review
    • Abstract: Publication date: Available online 24 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Xiaoli Chu, Bingzhen Sun, Qingchun Huang, Shouping Peng, Yingyan Zhou, Yan ZhangAbstractModern computer technology sheds light on new ways of innovating Traditional Chinese Medicine (TCM). One method that gets increasing attention is the quantitative research method, which makes use of data mining and artificial intelligence technology as well as the mathematical principles in the research on rationales, academic viewpoints of famous doctors of TCM, dialectical treatment by TCM, clinical technology of TCM, the patterns of TCM prescriptions, clinical curative effects of TCM and other aspects. This paper reviews the methods, means, progress and achievements of quantitative research on TCM. In the core database of the Web of Science, "Traditional Chinese Medicine", "Computational Science" and "Mathematical Computational Biology" are selected as the main retrieval fields, and the retrieval time interval from 1999 to 2019 is used to collect relevant literature. It is found that researchers from China Academy of Chinese Medical Sciences, Zhejiang University, Chinese Academy of Sciences and other institutes have opened up new methods of research on TCM since 2009, with quantitative methods and knowledge presentation models. The adopted tools mainly consist of text mining, knowledge discovery, technologies of the TCM database, data mining and drug discovery through TCM calculation, etc. In the future, research on quantitative models of TCM will focus on solving the heterogeneity and incompleteness of big data of TCM, establishing standardized treatment systems, and promoting the development of modernization and internationalization of TCM.
  • Prognostic Factors of Rapid Symptoms Progression in Patients with Newly
           Diagnosed Parkinson’s Disease
    • Abstract: Publication date: Available online 21 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Kostas M. Tsiouris, Spiros Konitsiotis, Dimitrios D. Koutsouris, Dimitrios I. FotiadisAbstractTracking symptoms progression in the early stages of Parkinson’s disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients’ condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson’s Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.
  • Comprehensive electrocardiographic diagnosis based on deep learning
    • Abstract: Publication date: Available online 20 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Oh Shu Lih, V Jahmunah, Tan Ru San, Edward J Ciaccio, Toshitaka Yamakawa, Masayuki Tanabe, Makiko Kobayashi, Oliver Faust, U Rajendra AcharyaAbstractCardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified on manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.
  • Scalogram based Prediction Model for Respiratory disorders using Optimized
           Convolutional Neural Networks
    • Abstract: Publication date: Available online 20 January 2020Source: Artificial Intelligence in MedicineAuthor(s): S. Jayalakshmy, Gnanou Florence SudhaAbstractAuscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. In this paper, a pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders. The proposed approach models the segmented respiratory sound signal into Bump and Morse scalograms from several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) method. From the extracted intrinsic mode functions, the percentage energy calculated for each wavelet coefficient in the form of scalograms are computed. Subsequently, these scalograms are given as input to the pre-trained optimized CNN model for training and testing. Stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms were examined to check the prediction accuracy on the dataset comprising of four classes of lung sounds, normal, crackles (coarse and fine), wheezes (monophonic & polyphonic) and low-pitched wheezes (Rhonchi). On comparison to the baseline method of standard Bump and Morse wavelet transform approach which produced 79.04% and 81.27% validation accuracy, an improved accuracy of 83.78% is achieved by the virtue of scalogram representation of various IMFs of EMD. Hence, the proposed approach achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature.
  • Measuring the effects of confounders in medical supervised classification
           problems: the Confounding Index (CI)
    • Abstract: Publication date: Available online 13 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Elisa Ferrari, Alessandra Retico, Davide BacciuAbstractOver the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data.
  • ADHD classification by dual subspace learning using resting-state
           functional connectivity
    • Abstract: Publication date: Available online 13 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Ying Chen, Yibin Tang, Chun Wang, Xiaofeng Liu, Li Zhao, Zhishun WangAbstractAs one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90% for most of ADHD databases in the leave-one-out cross-validation test.
  • Seven Pillars of Precision Digital Health and Medicine
    • Abstract: Publication date: Available online 11 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Arash Shaban-Nejad, Martin Michalowski, Niels Peek, John S. Brownstein, David L. Buckeridge
  • Comprenhensive analysis of rule formalisms to represent clinical
           guidelines: Selection criteria and case study on antibiotic clinical
    • Abstract: Publication date: Available online 9 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Natalia Iglesias, Jose M. Juarez, Manuel CamposAbstractBackgroundThe over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guidelines (CGs) focus on the correct prescription of antibiotics in a narrative form, Clinical Decision Support Systems (CDSS) operationalize the knowledge contained in CGs in the form of rules at the point of care. Despite the efforts made to computerize CGs, there is still a gap between CGs and the myriad of rule technologies (based on different logic formalisms) that are available to implement CDSSs in real clinical settings.ObjectiveTo helpCDSS designers to determine the most suitable rule-based technology (medical-oriented rules, production rules and semantic web rules) with which to model knowledge from CGs for the prescription of antibiotics. We propose a framework of criteria for this purpose that is extensible to more generic CGs.Materials and methodsOur proposal is based on the identification of core technical requirements extracted from both literature and the analysis of CGs for antibiotics, establishing three dimensions for analysis: language expressivity, interoperability and industrial aspects. We present a case study regarding the John Hopkins Hospital (JHH) Antibiotic Guidelines for Urinary Tract Infection (UTI), a highly recurring hospital acquired infection. We have adopted our framework of criteria in order to analyse and implement these CGs using various rule technologies: HL7 Arden Syntax, general-purpose Production Rules System (Drools), HL7 standard Rule Interchange Format (RIF), Semantic Web Rule Language (SWRL) and SParql Inference Notation (SPIN) rule extensions (implementing our own ontology for UTI).ResultsWe have identified the main criteria required to attain a maintainable and cost-affordable computable knowledge representation for CGs. We have represented the JHH UTI CGs knowledge in a total of 12 Arden Syntax MLMs, 81 Drools rules and 154 ontology classes, properties and individuals. Our experiments confirm the relevance of the proposed set of criteria and show the level of compliance of the different rule technologies with the JHH UTI CGs knowledge representation.ConclusionsThe proposed framework of criteria may help clinical institutions to select the most suitable rule technology for the representation of CGs in general, and for the antibiotic prescription domain in particular, depicting the main aspects that lead to Computer Interpretable Guidelines (CIGs), such as Logic expressivity (Open/Closed World Assumption, Negation-As-Failure), Temporal Reasoning and Interoperability with existing HIS and clinical workflow. Future work will focus on providing clinicians with suggestions regarding new potential steps for CGs, considering process mining approaches and CGs Process Workflows, the use of HL7 FHIR for HIS interoperability and the representation of Knowledge-as- a-Service (KaaS).
  • Optimisation and control of the supply of blood bags in hemotherapic
           centres via Markov Decision Process with discounted arrival rate
    • Abstract: Publication date: Available online 8 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Henrique L.F. Soares, Edilson F. Arruda, Laura Bahiense, Daniel Gartner, Luiz Amorim FilhoAbstractRunning a cost-effective human blood transfusion supply chain challenges decision makers in blood services world-wide. In this paper, we develop a Markov decision process with the objective of minimising the overall costs of internal and external collections, storing, producing and disposing of blood bags, whilst explicitly considering the probability that a donated blog bag will perish before demanded. The model finds an optimal policy to collect additional bags based on the number of bags in stock rather than using information about the age of the oldest item. Using data from the literature, we validate our model and carry out a case study based on data from a large blood supplier in South America. The study helped achieve an overall increase of 4.5% in blood donations in one year.
  • An effective approach for CT lung segmentation using mask region-based
           convolutional neural networks
    • Abstract: Publication date: Available online 8 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Qinhua Hu, Luís Fabrício de F. Souza, Gabriel Bandeira Holanda, Shara S.A. Alves, Francisco Hércules dos S. Silva, Tao Han, Pedro P. Rebouças FilhoComputer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung regions in the CT images are marked manually by a specialist as this initial step is a significant challenge for computer vision techniques. Once defined, the lung regions are segmented for clinical diagnoses. This work proposes an automatic segmentation of the lungs in CT images, using the Convolutional Neural Network (CNN) Mask R-CNN, to specialize the model for lung region mapping, combined with supervised and unsupervised machine learning methods (Bayes, Support Vectors Machine (SVM), K-means and Gaussian Mixture Models (GMMs)). Our approach using Mask R-CNN with the K-means kernel produced the best results for lung segmentation reaching an accuracy of 97.68 ± 3.42% and an average runtime of 11.2 s. We compared our results against other works for validation purposes, and our approach had the highest accuracy and was faster than some state-of-the-art methods.Graphical abstractGraphical abstract for this article
  • An intelligent learning approach for improving ECG signal classification
           and arrhythmia analysis
    • Abstract: Publication date: March 2020Source: Artificial Intelligence in Medicine, Volume 103Author(s): Arun Kumar Sangaiah, Maheswari Arumugam, Gui-Bin BianAbstractThe recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The main features extracted in the proposed work are minimum, maximum, mean, standard deviation, and median. The experiments were conducted on forty-five ECG records in MIT BIH arrhythmia database and in MIT BIH noise stress test database. The proposed model has an overall accuracy of 99.7 % with a sensitivity of 99.7 % and a positive predictive value of 100 %. The detection error rate for the proposed model is 0.0004. This paper also includes a study of the cardiac arrhythmia recognition using an IoMT (Internet of Medical Things) approach.
  • Multi-planar 3D breast segmentation in MRI via deep convolutional neural
    • Abstract: Publication date: March 2020Source: Artificial Intelligence in Medicine, Volume 103Author(s): Gabriele Piantadosi, Mario Sansone, Roberta Fusco, Carlo SansoneAbstractNowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual DCE-MRI examination can be difficult and error-prone. The early stage of breast tissue segmentation, in a typical CAD, is crucial to increase reliability and reduce the computational effort by reducing the number of voxels to analyze and removing foreign tissues and air. In recent years, the deep convolutional neural networks (CNNs) enabled a sensible improvement in many visual tasks automation, such as image classification and object recognition. These advances also involved radiomics, enabling high-throughput extraction of quantitative features, resulting in a strong improvement in automatic diagnosis through medical imaging. However, machine learning and, in particular, deep learning approaches are gaining popularity in the radiomics field for tissue segmentation. This work aims to accurately segment breast parenchyma from the air and other tissues (such as chest-wall) by applying an ensemble of deep CNNs on 3D MR data. The novelty, besides applying cutting-edge techniques in the radiomics field, is a multi-planar combination of U-Net CNNs by a suitable projection-fusing approach, enabling multi-protocol applications. The proposed approach has been validated over two different datasets for a total of 109 DCE-MRI studies with histopathologically proven lesions and two different acquisition protocols. The median dice similarity index for both the datasets is 96.60 % (±0.30 %) and 95.78 % (±0.51 %) respectively with p 
  • A fusion framework to extract typical treatment patterns from electronic
           medical records
    • Abstract: Publication date: March 2020Source: Artificial Intelligence in Medicine, Volume 103Author(s): Jingfeng Chen, Leilei Sun, Chonghui Guo, Yanming XieAbstractObjectiveElectronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify “right patient”, “right drug”, “right dose”, “right route”, and “right time” from doctor order information.MethodsWe propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method. The multi-view SNF method involves three similarity measures: content-view similarity, sequence-view similarity and duration-view similarity. An EMR dataset and two metrics were utilized to evaluate the performance and to extract typical treatment patterns.ResultsExperimental results on a real-world EMR dataset show that the multi-view similarity network fusion method outperforms all the single-view similarity measures and also outperforms the existing similarity measure methods. Furthermore, we extract and visualize typical treatment patterns by clustering analysis.ConclusionThe extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice.
  • Gait characteristics and clinical relevance of hereditary spinocerebellar
           ataxia on deep learning
    • Abstract: Publication date: Available online 7 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Luya Jin, Wen Lv, Guocan Han, Linhui Ni, Di sun, Xingyue Hu, Huaying CaiAbstractBackgroundDeep learning has always been at the forefront of scientific research. It has also been applied to medical research. Hereditary spinocerebellar ataxia (SCA) is characterized by gait abnormalities and is usually evaluated semi-quantitatively by scales. However, more detailed gait characteristics of SCA and related objective methods have not yet been established. Therefore, the purpose of this study was to evaluate the gait characteristics of SCA patients, as well as to analyze the correlation between gait parameters, clinical scales, and imaging on deep learning.MethodsTwenty SCA patients diagnosed by genetic detection were included in the study. Ten patients who were tested via functional magnetic resonance imaging (fMRI) were included in the SCA imaging subgroup. All SCA patients were evaluated with the International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA) clinical scales. The gait control group included 16 healthy subjects, and the imaging control group included seven healthy subjects. Gait data consisting of 10 m of free walking of each individual in the SCA group and the gait control group were detected by wearable gait-detection equipment. Stride length, stride time, velocity, supporting-phase percentage, and swinging-phase percentage were extracted as gait parameters. Cerebellar volume and the midsagittal cerebellar proportion in the posterior fossa (MRVD) were calculated according to MR.ResultsThere were significant differences in stride length, velocity, supporting-phase percentage, and swinging-phase percentage between the SCA group and the gait control group. The stride length and stride velocity of SCA groups were lower while supporting phase was longer than those of the gait control group. SCA group's velocity was negatively correlated with both the ICARS and SARA scores. The cerebellar volume and MRVD of the SCA imaging subgroup were significantly smaller than those of the imaging control group. MRVD was significantly correlated with ICARS and SARA scores, as well as stride velocity variability.ConclusionSCA gait parameters were characterized by a reduced stride length, slower walking velocity, and longer supporting phase. Additionally, a smaller cerebellar volume correlated with an increased irregularity in gait. Gait characteristics exhibited considerable clinical relevance to hereditary SCA. We conclude that a combination of gait parameters, ataxia scales, and MRVD may represent more objective markers for clinical evaluations of SCA.
  • An Improved Multi-swarm Particle Swarm Optimizer for Optimizing the
           Electric Field Distribution of Multichannel Transcranial Magnetic
    • Abstract: Publication date: Available online 3 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Hui Xiong, Bowen Qiu, Jinzhen LiuAbstractMultichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of double layer coil array. To balance the exploration and exploitation abilities, three novel improved strategies are used in NMSPSO based on multi-swarm particle swarm optimizer. Firstly, a novel information exchange strategy is achieved by individual exchanges between sub-swarms. Secondly, a novel leaning strategy is used to control knowledge dissemination in the population, which not only increases the diversity of the particles but also guarantees the convergence. Finally, a novel mutation strategy is introduced, which can help the population jump out of the local optimum for better exploration ability. The method is examined on a set of well-known benchmark functions and the results show that NMSPSO has better performance than many particle swarm optimization variants. And the superior electric field distribution in mTMS can be obtained by NMSPSO to optimize the current configuration of the double layer coil array.
  • An enhanced deep learning approach for brain cancer MRI images
           classification using residual networks
    • Abstract: Publication date: January 2020Source: Artificial Intelligence in Medicine, Volume 102Author(s): Sarah Ali Abdelaziz Ismael, Ammar Mohammed, Hesham HefnyAbstractCancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.
  • Predicting dementia with routine care EMR data
    • Abstract: Publication date: January 2020Source: Artificial Intelligence in Medicine, Volume 102Author(s): Zina Ben Miled, Kyle Haas, Christopher M. Black, Rezaul Karim Khandker, Vasu Chandrasekaran, Richard Lipton, Malaz A. BoustaniAbstractOur aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia.Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions.The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.
    • Abstract: Publication date: Available online 31 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Venkatachalam K., Devipriya A., Maniraj J., Sivaram M., Ambikapathy A., S Amiri IrajAbstractA subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, neuro-prosthetics and neuro-rehabilitation, where the user’s thoughts of imagined movements need to be decoded. Electroencephalography (EEG) equipment is commonly used for keeping track of cerebrum movement in BCI systems. The EEG signals are recognized by feature extraction and classification. The current research proposes a Hybrid-KELM (Kernel Extreme Learning Machine) method based on PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) for MI BCI classification of EEG data. The performance and results of the method are demonstrated using BCI competition dataset III, and compared with those of contemporary methods. The proposed method generated an accuracy of 96.54%.
  • Semantic Segmentation with DenseNets for Carotid Artery Ultrasound Plaque
           Segmentation and CIMT estimation
    • Abstract: Publication date: Available online 31 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Maria del Mar Vila, Beatriz Remeseiro, Maria Grau, Roberto Elosua, Àngels Betriu, Elvira Fernandez-Giraldez, Laura IgualAbstractBackground and ObjectiveThe measurement of Carotid Intima Media Thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: 1) a manual examination of the ultrasound image for the localization of a Region Of Interest (ROI), a fast and useful operation when only a small number of images need to be measured; and 2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images.MethodsOur single-step approach is based on Densely Connected Convolutional Neural Networks (DenseNets) for semantic segmentation of the whole image. It has two remarkable characteristics: (1) it avoids ROI definition, and (2) it captures multi-scale contextual information in the complete image interpretation, due to the concatenation of feature maps carried out in DenseNets. Once the input image is segmented, a straightforward method for CIMT estimation and plaque detection is applied.ResultsThe proposed method has been validated with a large data set (REGICOR) of more than 8,000 images, corresponding to two territories of the Carotid Artery: Common Carotid Artery (CCA) and Bulb. Among them, a subset of 331 images has been used to evaluate the performance of semantic segmentation (≈90% for train, ≈10% for test). The experimental results demonstrated that our method outperforms other deep models and shallow approaches found in the literature. In particular, our CIMT estimation reaches a correlation coefficient of 0.81, and a CIMT mean error of 0.02 mm and 0.06 mm in CCA and Bulb images, respectively. Furthermore, the accuracy for plaque detection is 96.45% and 78.09% in CCA and Bulb, respectively. To test the generalization power, the method has also been tested with another data set (NEFRONA) that includes images acquired with different equipment.ConclusionsThe validation carried out demonstrates that the proposed method is accurate and objective for both plaque detection and CIMT measurement. Moreover, the robustness and generalization capacity of the method have been proven with two different data sets.
  • Topic-informed neural approach for biomedical event extraction
    • Abstract: Publication date: Available online 30 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Junchi Zhang, Mengchi Liu, Yue ZhangAbstractAs a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus.
  • Medical Knowledge Embedding Based on Recursive Neural Network for
           Multi-Disease Diagnosis
    • Abstract: Publication date: Available online 28 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Jingchi Jiang, Huanzheng Wang, Jing Xie, Xitong Guo, Yi Guan, Qiubin YuAbstractThe representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases.
  • Fully-automated deep learning-powered system for DCE-MRI analysis of brain
    • Abstract: Publication date: Available online 27 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Jakub Nalepa, Pablo Ribalta Lorenzo, Michal Marcinkiewicz, Barbara Bobek-Billewicz, Pawel Wawrzyniak, Maksym Walczak, Michal Kawulok, Wojciech Dudzik, Krzysztof Kotowski, Izabela Burda, Bartosz Machura, Grzegorz Mrukwa, Pawel Ulrych, Michael P. HayballAbstractDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a real-time algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results while requiring less than 3 minutes to process an entire input DCE-MRI study using a single GPU.
  • Evidence of the benefits, advantages and potentialities of the structured
           radiological report: an integrative review
    • Abstract: Publication date: Available online 25 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Douglas M. Rocha, Lourdes M. Brasil, Janice M. Lamas, Glécia V.S. Luz, Simônides S. BacelarAbstractThe structured report is a new trend for the preparation and manipulation of radiological examination reports. The structuring of the radiological report data can bring many benefits and advantages over other existing methodologies. Research and studies about the structured radiological report are highly relevant in clinical and academic subjects, improving medical practice, reducing unobserved problems by radiologists, improving reporting practices and medical diagnoses. Exposing the benefits, advantages and potential of the structured radiological report is important in encouraging the acceptance and implementation of this method by radiology professionals who are still somewhat resistant. The present review highlights the factors that contribute to the consolidation of adopting the structured radiology report methodology, addressing a variety of studies focused on the structuring of the radiological report. This integrative review of the literature is proposed by searching publications and journals databases (CAPES - Coordination of Improvement of Higher-Level Personnel, SciELO - Scientific Electronic Library Online, and PubMed - Publisher Medline) to develop a complete and unified understanding of the subject, so that it becomes a major part of evidence-based initiatives.
  • An improved fuzzy set-based multifactor dimensionality reduction for
           detecting epistasis
    • Abstract: Publication date: Available online 22 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Cheng-Hong Yang, Li-Yeh Chuang, Yu-Da LinAbstractObjectiveEpistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.Methods and materialIn this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes.ResultsWe compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation.ConclusionFSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.
  • Ophthalmic Diagnosis Using Deep Learning with Fundus Images - A Critical
    • Abstract: Publication date: Available online 22 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Sourya Sengupta, Amitojdeep Singh, Henry A. Leopold, Tanmay Gulati, Vasudevan LakshminarayananAbstractAn overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk,optic cup, blood vessels as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma and diabetic retinopathy are also discussed. Important critical insights and future research directions have been given.
    • Abstract: Publication date: Available online 21 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Geer Teng, Yue He, Hengjun Zhao, Dunhu Liu, Jin Xiao, S. RamkumarAbstractToday’s life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks. The classification results of 92.17% and 91.85% were shown for band power and HHT features using PRNN architecture. Recognition accuracy was analyzed in offline to identify the possibilities of designing HCI. We compare the two feature extraction techniques with PRNN to analyze the best method for classifying the tasks and recognizing single trail tasks to design the HCI. Our experimental result confirms that for classifying as well as recognizing accuracy of the collected signals using band power with PRNN shows better accuracy compared to other network used in this study. We compared the male subjects performance with female subjects to identify the performance. Finally we compared the male as well as female subjects in age group wise to identify the performance of the system. From that we concluded that male performance was appreciable compared with female subjects as well as age group between 26 to 32 performance and recognizing accuracy were high compared with other age groups used in this study.
  • Disease phenotype synonymous prediction through network representation
           learning from PubMed database
    • Abstract: Publication date: Available online 19 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Shiwen Ma, Kuo Yang, Ning Wang, Qiang Zhu, Zhuye Gao, Runshun Zhang, Baoyan Liu, Xuezhong ZhouAbstractSynonym mapping between phenotype concepts from different terminologies is difficult because terminology databases have been developed largely independently. Existing maps of synonymous phenotype concepts from different terminology databases are highly incomplete, and manually mapping is time consuming and laborious. Therefore, building an automatic method for predictive mapping of synonymous phenotypes is of special importance. We propose a classifier-based phenotype mapping prediction model (CPM) to predict synonymous relationships between phenotype concepts from different terminology databases. The model takes network semantic representations of phenotypes as input and predicts synonymous relationships by training binary classifiers with a voting strategy. We compared the performance of the CPM with a similarity-based phenotype mapping prediction model (SPM), which predicts mapping based on the ranked cosine similarity of candidate mapping concepts. Based on a network representation N2V-TFIDF, with a majority voting strategy method MV, the CPM achieved accuracy of 0.943, which was 15.4% higher than that of the SPM using the cosine similarity method (0.789) and 23.8% higher than that of the SSDTM method (0.724) proposed in our previous work.
  • Electroencephalogram Based Communication System for Locked In State Person
           Using Mentally Spelled Tasks with Optimized Network Model
    • Abstract: Publication date: Available online 19 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Xu Xiaoxiao, Luo Bin, S. Ramkumar, S Saravanan, M. Sundar Prakash Balaji, S. Dhanasekaran, J. ThimmiarajaAbstractDue to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86% then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50%. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.
  • Deep Supervised Learning with Mixture of Neural Networks
    • Abstract: Publication date: Available online 18 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Yaxian Hu, Senlin Luo, Longfei Han, Limin Pan, Tiemei ZhangAbstractDeep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single model to perform well on the classification tasks. In this paper, we propose mixture model based on DNNs (MoNNs), a supervised approach to perform classification tasks with a gating network and multiple local expert models. We use a neural network as a gating function and use DNNs as local expert models. The gating network split the heterogeneous data into several homogeneous components. DNNs are combined to perform classification tasks in each component. Moreover, we use EM (Expectation Maximization) as an optimization algorithm. Experiments proved that our MoNNs outperformed the other compared methods on determination of diabetes, determination of benign or malignant breast cancer, and handwriting recognition. Therefore, the MoNNs can solve the problem of data heterogeneity and have a good effect on classification tasks.
  • Artificial Intelligence and the Future of Psychiatry: Insights from a
           Global Physician Survey
    • Abstract: Publication date: Available online 18 November 2019Source: Artificial Intelligence in MedicineAuthor(s): P. Murali Doraiswamy, Charlotte Blease, Kaylee BodnerAbstractBackgroundFuturists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appears ripe for such disruption given the global illness burden, stigma, and shortage of care providers.ObjectiveTo characterize the global psychiatrist community’s opinion regarding the potential of future autonomous technology (referred to here as AI/ML) to replace key tasks carried out in mental health practice.DesignCross sectional, random stratified sample of psychiatrists registered with Sermo, a global networking platform open to verified and licensed physicians.Main outcome measuresWe measured opinions about the likelihood that AI/ML tools would be able to fully replace – not just assist – the average psychiatrist in performing 10 key psychiatric tasks. Among those who considered replacement likely, we measured opinions about how many years from now such a capacity might emerge. We also measured psychiatrist’s perceptions about whether benefits of AI/ML would outweigh the risks.ResultsSurvey respondents were 791 psychiatrists from 22 countries representing North America, South America, Europe and Asia-Pacific. Only 3.8% of respondents felt it was likely that future technology would make their jobs obsolete and only 17% felt that future AI/ML was likely to replace a human clinician for providing empathetic care. Documenting and updating medical records (75%) and synthesizing information (54%) were the two tasks where a majority predicted that AI/ML could fully replace human psychiatrists. Female- and US-based doctors were more uncertain that the benefits of AI would outweigh risks than male- and non-US doctors, respectively. Around one in 2 psychiatrists did however predict that their jobs would be substantially changed by AI/ML.ConclusionsOur findings provide compelling insights into how physicians think about AI/ML which in turn may help us better integrate technology and reskill doctors to enhance mental health care.
  • Clinical Decision Support Systems for Triage in the Emergency Department
           using Intelligent Systems: a Review
    • Abstract: Publication date: Available online 17 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Marta Fernandes, Susana M. Vieira, Francisca Leite, Carlos Palos, Stan Finkelstein, João M.C. SousaMotivationEmergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage.ObjectivesThe objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation.MethodsWe applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems.ResultsFrom the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient's prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage.ConclusionsIn the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.
  • Multi-objective evolutionary design of antibiotic treatments
    • Abstract: Publication date: Available online 17 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Gabriela Ochoa, Lee A. Christie, Alexander E. Brownlee, Andrew HoyleAbstractAntibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. The design of antibiotic treatments can be formulated as an optimisation problem where candidate solutions are encoded as vectors of dosages per day. The formulation naturally gives rise to competing objectives, as we want to maximise the treatment effectiveness while minimising the total drug use, the treatment duration and the concentration of antibiotic experienced by the patient. This article combines a recent mathematical model of bacterial growth including both susceptible and resistant bacteria, with a multi-objective evolutionary algorithm in order to automatically design successful antibiotic treatments. We consider alternative formulations combining relevant objectives and constraints. Our approach obtains shorter treatments, with improved success rates and smaller amounts of drug than the standard practice of administering daily fixed doses. These new treatments consistently involve a higher initial dose followed by lower tapered doses.
  • Prediction of fetal weight at varying gestational age in the absence of
           ultrasound examination using ensemble learning
    • Abstract: Publication date: Available online 17 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Yu Lu, Xianghua Fu, Fangxiong Chen, Kelvin K.L. WongAbstractObstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is that ultrasound estimation of fetal weight is subject to populations’ difference, strict operating requirements for sonographers, and poor access to ultrasound in low-resource areas. Inaccurate estimations may lead to negative perinatal outcomes. This study aims to predict fetal weight at varying gestational age in the absence of ultrasound examination within a certain accuracy. We consider that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring. We present a robust methodology using a data set comprising 4,212 intrapartum recordings. The cubic spline function is used to fit the curves of several key characteristics that are extracted from ultrasound reports. A number of simple and powerful machine learning algorithms are trained, and their performance is evaluated with real test data. We also propose a novel evaluation performance index called the intersection-over-union (loU) for our study. The results are encouraging using an ensemble model consisting of Random Forest, XGBoost, and LightGBM algorithms. The experimental results show the loU between predicted range of fetal weight at any gestational age from the ensemble model and that from ultrasound. The machine learning based approach applied in our study is able to predict, with a high accuracy, fetal weight at varying gestational age in the absence of ultrasound examination.
  • State recognition of decompressive laminectomy with multiple information
           in robot-assisted surgery
    • Abstract: Publication date: Available online 16 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Yu Sun, Li Wang, Zhongliang Jiang, Bing Li, Ying Hu, Wei TianAbstractThe decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons have limited perception during remote-controlled robot-assisted surgery. The novelty of this paper is that a state recognition system is proposed for the robot-assisted tele-surgery. By combining the learning methods and traditional methods, the robot from the slave-end can think about the current operation state like a surgeon, and provide more information and decision suggestions to the master-end surgeon, which aids surgeons work safer in tele-surgery. For the fenestration, we propose an image-based state recognition method that consists a U-Net derived network, grayscale redistribution and dynamic receptive field assisting in controlling the grinding process to prevent the grinding-bit from crossing the inner edge of the lamina to damage the spinal nerves. For the internal fixation, we propose an audio and force-based state recognition method that consists signal features extraction methods, LSTM-based prediction and information fusion assisting in monitoring the drilling process to prevent the drilling-bit from crossing the outer edge of the vertebral pedicle to damage the spinal nerves. Several experiments are conducted to show the reliability of the proposed system in robot-assisted surgery.
  • A modular cluster based collaborative recommender system for cardiac
    • Abstract: Publication date: Available online 16 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Anam Mustaqeem, Syed Muhammad Anwar, Muhammad MajidAbstractIn the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Among health recommender systems, collaborative filtering technique has gained a significant success. However, traditional collaborative filtering algorithms are facing challenges such as data sparsity and scalability, which leads to a reduction in system accuracy and efficiency. In a clinical setting, the recommendations should be accurate and timely. In this paper, an improvised collaborative filtering technique is proposed, which is based on clustering and sub-clustering. The proposed methodology is applied on a supervised set of data for four different types of cardiovascular diseases including angina, non-cardiac chest pain, silent ischemia, and myocardial infarction. The patient data is partitioned with respect to their corresponding disease class, which is followed by k-mean clustering, applied separately on each disease partition. A query patient once directed to the correct disease partition requires to get similarity scores from a reduced sub-cluster, thereby improving the efficiency of the system. Each disease partition has a separate process for recommendation, which gives rise to modularization and helps in improving scalability of the system. The experimental results demonstrate that the proposed modular clustering based recommender system reduces the spatial search domain for a query patient and the time required for providing accurate recommendations. The proposed system improves upon the accuracy of recommendations as demonstrated by the precision and recall values. This is significant for health recommendation systems particularly for those related to cardiovascular diseases.
  • Pressure injury image analysis with machine learning techniques: A
           systematic review on previous and possible future methods
    • Abstract: Publication date: Available online 13 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Sofia Zahia, Maria Begoña Garcia Zapirain, Xavier Sevillano, Alejandro González, Paul J. Kim, Adel ElmaghrabyPressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.Graphical abstractGraphical abstract for this article
  • Using multi-layer perceptron with Laplacian edge detector for bladder
           cancer diagnosis
    • Abstract: Publication date: Available online 13 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Ivan Lorencin, Nikola Anđelić, Josip Španjol, Zlatan CarAbstractIn this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.
  • A multi-context CNN ensemble for small lesion detection
    • Abstract: Publication date: Available online 13 November 2019Source: Artificial Intelligence in MedicineAuthor(s): B. Savelli, A. Bria, M. Molinara, C. Marrocco, F. TortorellaAbstractIn this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.
  • A methodology based on multiple criteria decision analysis for combining
           antibiotics in empirical therapy
    • Abstract: Publication date: Available online 13 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Manuel Campos, Fernando Jimenez, Gracia Sanchez, Jose M. Juarez, Antonio Morales, Bernardo Canovas-Segura, Francisco PalaciosBackgroundThe current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspect for selecting an appropriate and adequate antimicrobial therapy is that prescription must be based on local epidemiology and knowledge since many aspects, such as prevalence of microorganisms and effectiveness of antimicrobials, change from hospitals, or even areas and services within a single hospital. Therefore, the selection of combinations of antibiotics requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis of the detailed microbiological, epidemiological, pharmacological information on which to base a rational and reasoned choice.MethodsWe proposed a methodology for decision making that uses a multiple criteria decision analysis (MCDA) to support the clinician in the selection of an efficient combined empiric therapy. The MCDA includes a multi-objective constrained optimization model whose criteria are the maximum efficacy of therapy, maximum activity, the minimum activity overlapping, the minimum use of restricted antibiotics, the minimum toxicity of antibiotics and the activity against the most prevalent and virulent bacteria. The decision process can be defined in 4 steps: (1) selection of clinical situation of interest, (2) definition of local optimization criteria, (3) definition of constraints for reducing combinations, (4) manual sorting of solutions according to patient's clinical conditions, and (5) selection of a combination.Experiments and resultsIn order to show the application of the methodology to a clinical case, we carried out experiments with antibiotic susceptibility tests in blood samples taken during a five years period at a university hospital. The validation of the results consists of a manual review of the combinations and experiments carried out by an expert physician that has explained the most relevant solutions proposed according to current clinical knowledge and their use.ConclusionWe show that with the decision process proposed, the physician is able to select the best combined therapy according to different criteria such as maximum efficacy, activity and minimum toxicity. A method for the recommendation of combined antibiotic therapy developed on the basis of a multi-objective optimization model may assist the physicians in the search for alternatives to the use of broad-spectrum antibiotics or restricted antibiotics for empirical therapy. The decision proposed can be easily reproduced for any local epidemiology and any different clinical settings.Graphical abstractGraphical abstract for this article
  • Optimized artificial neural network based performance analysis of
           wheelchair movement for ALS patients
    • Abstract: Publication date: Available online 9 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Kai Li, S. Ramkumar, J. Thimmiaraja, S. DiwakaranAbstractIndividuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speaking impairment and totally make the person in to locked in state (LIS). To overcome the miserable condition the person need rehabilitation devices through a Brain Computer Interfaces (BCI) to satisfy their needs. BMI using Electroencephalogram (EEG) receives the mental thoughts from brain and converts into control signals to activate the exterior communication appliances in the absence of biological channels. To design the BCI, we conduct our study with three normal male subjects, three normal female subjects and three ALS affected individuals from the age of 20 to 60 with three electrode systems for four tasks. One Dimensional Local Binary Patterns (LBP) technique was applied to reduce the digitally sampled features collected from nine subjects was treated with Grey wolf optimization Neural Network (GWONN) to classify the mentally composed words. Using these techniques, we compared the three types of subjects to identify the performances. The study proves that subjects from normal male categories performance was maximum compared with the other subjects. To assess the individual performance of the subject, we conducted the recognition accuracy test in offline mode. From the accuracy test also, we obtained the best performance from the normal male subjects compared with female and ALS subjects with an accuracy of 98.33%, 95.00% and 88.33%. Finally our study concludes that patients with ALS attack need more training than that of the other subjects.
  • Artificial Plant Optimization Algorithm to detect Heart Rate & Presence of
           Heart Disease using Machine Learning
    • Abstract: Publication date: Available online 8 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Prerna Sharma, Krishna Choudhary, Kshitij Gupta, Rahul Chawla, Deepak Gupta, Arun SharmaAbstractIn today’s world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can take precautions or treatment before it becomes fatal. For serving this purpose a Modified Artificial Plant Optimization (MAPO) algorithm has been proposed which can be used as an optimal feature selector along with other machine learning algorithms to predict the heart rate using the fingertip video dataset which further predicts the presence or absence of Coronary Heart Disease in an individual at the moment. Initially, the video dataset has been pre-processed, noise is filtered and then MAPO is applied to predict the heart rate with a Pearson correlation and Standard Error Estimate of 0.9541 and 2.418 respectively. The predicted heart rate is used as a feature in other two datasets and MAPO is again applied to optimize the features of both datasets. Different machine learning algorithms are then applied to the optimized dataset to predict values for presence of current heart disease. The result shows that MAPO reduces the dimensionality to the most significant information with comparable accuracies for different machine learning models with maximum dimensionality reduction of 81.25%. MAPO has been compared with other optimizers and outperforms them with better accuracy.
  • Skin Cancer Diagnosis Based on Optimized Convolutional Neural Network
    • Abstract: Publication date: Available online 8 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Ni Zhang, Yi-Xin Cai, Yong-Yong Wang, Yi-Tao Tian, Xiao-Li Wang, Benjamin BadamiAbstractEarly detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.
  • Signal identification system for developing Rehabilittive device using
           deep learning algorithms
    • Abstract: Publication date: Available online 8 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Wenping Tang, Aiqun Wang, S. Ramkumar, Radeep Krishna Radhakrishnan NairAbstractParalyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to migrate. To overcome from their problem they need some assistive technology with the help of bio signals. Electrooculogram (EOG) based Human Computer Interaction (HCI) is one of the technique used in recent days to overcome such problem. In this paper we clearly check the possibilities of creating nine states HCI by our proposed method. Signals were captured through five electrodes placed on the subjects face around the eyes. These signals were amplified with ADT26 bio amplifier, filtered with notch filter, and processed with reference power and band power techniques to extract features to detect the eye movements and mapped with Time Delay Neural Network to classify the eye movements to generate control signal to control external hardware devices. Our experimental study reports that maximum average classification of 91.09% for reference power feature and 91.55%-for band power feature respectively. The obtained result confirms that band power features with TDNN network models shows better performance than reference features for all subjects. From this outcome we conclude that band power features with TDNN network models was more suitable for classifying the eleven difference eye movements for individual subjects. To validate the result obtained from this method we categorize the subjects in age wise to check the accuracy of the system. Single trail analysis was conducted in offline to identify the recognizing accuracy of the proposed system. The result summarize that band power features with TDNN network models exceed the reference power with TDNN network model used in this study. Through the outcome we conclude that that band power features with TDNN network was more suitable for designing EOG based HCI in offline mode.
  • Multi-resolution Convolutional Networks for Chest X-Ray Radiograph Based
           Lung Nodule Detection
    • Abstract: Publication date: Available online 28 October 2019Source: Artificial Intelligence in MedicineAuthor(s): Xuechen Li, Linlin Shen, Xinpeng Xie, Shiyun Huang, Zhien Xie, Xian Hong, Juan YuAbstractLung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.
  • Automatic Detection of Epileptic Seizure Based on Approximate Entropy,
           Recurrence Quantification Analysis and Convolutional Neural Networks
    • Abstract: Publication date: Available online 7 September 2019Source: Artificial Intelligence in MedicineAuthor(s): Xiaozeng Gao, Xiaoyan Yan, Ping Gao, Xiujiang Gao, Shubo ZhangAbstractEpilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind epileptic disorders. Approximate entropy and recurrence quantification analysis are nonlinear analysis tools to quantify the complexity and recurrence behaviors of non-stationary signals, respectively. Convolutional neural networks are powerful class of models. In this paper, a new method for automatic epileptic electroencephalogram recordings based on the approximate entropy and recurrence quantification analysis combined with a convolutional neural network were proposed. The Bonn dataset was used to assess the proposed approach. The results indicated that the performance of the epileptic seizure detection by approximate entropy and recurrence quantification analysis is good (all of the sensitivities, specificities and accuracies are greater than 80%); especially the sensitivity, specificity and accuracy of the recurrence rate achieved 92.17%, 91.75% and 92.00%. When combines the approximate entropy and recurrence quantification analysis features with convolutional neural networks to automatically differentiate seizure electroencephalogram from normal recordings, the classification result can reach to 98.84%, 99.35% and 99.26%. Thus, this makes automatic detection of epileptic recordings become possible and it would be a valuable tool for the clinical diagnosis and treatment of epilepsy.
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