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  Subjects -> MEDICAL SCIENCES (Total: 8250 journals)
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Showing 1 - 200 of 3562 Journals sorted alphabetically
16 de Abril     Open Access   (Followers: 1)
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: 7)
Abia State University Medical Students' Association Journal     Full-text available via subscription   (Followers: 2)
ACIMED     Open Access   (Followers: 1)
ACS Medicinal Chemistry Letters     Hybrid Journal   (Followers: 46)
Acta Bio Medica     Full-text available via subscription   (Followers: 2)
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Acta Bioquimica Clinica Latinoamericana     Open Access   (Followers: 1)
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Acupuncture & Electro-Therapeutics Research     Full-text available via subscription   (Followers: 6)
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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)
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Advanced Biomedical Research     Open Access  
Advanced Health Care Technologies     Open Access   (Followers: 8)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 8)
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Advances in Parkinson's Disease     Open Access  
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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: 3)
African Journal of Biomedical Research     Open Access   (Followers: 1)
African Journal of Clinical and Experimental Microbiology     Open Access   (Followers: 2)
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 Primary Research     Partially Free   (Followers: 3)
AJSP: Reviews & Reports     Hybrid Journal  
Aktuelle Ernährungsmedizin     Hybrid Journal   (Followers: 4)
Al-Azhar Assiut Medical Journal     Open Access   (Followers: 2)
ALERTA : Revista Científica del Instituto Nacional de Salud     Open Access  
Alexandria Journal of Medicine     Open Access   (Followers: 1)
Allgemeine Homöopathische Zeitung     Hybrid Journal   (Followers: 4)
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: 5)
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: 50)
American Journal of Medicine and Medical Sciences     Open Access   (Followers: 1)
American Journal of Medicine Studies     Open Access   (Followers: 2)
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: 5)
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: 1)
Anatomy     Open Access   (Followers: 2)
Anatomy Research International     Open Access   (Followers: 3)
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)
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Annals of Biomedical Engineering     Hybrid Journal   (Followers: 19)
Annals of Biomedical Sciences     Full-text available via subscription   (Followers: 4)
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Annals of Clinical Microbiology and Antimicrobials     Open Access   (Followers: 12)
Annals of Family Medicine     Open Access   (Followers: 14)
Annals of Health Research     Open Access   (Followers: 1)
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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: 11)
Annals of Nigerian Medicine     Open Access   (Followers: 1)
Annals of Rehabilitation Medicine     Open Access  
Annals of Saudi Medicine     Open Access  
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)
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Archives of Medicine and Surgery     Open Access  
Archives of Trauma Research     Open Access   (Followers: 3)
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     Full-text available via subscription   (Followers: 17)
Arterial Hypertension     Open Access   (Followers: 1)
Artificial Intelligence in Medicine     Hybrid Journal   (Followers: 17)
Artificial Organs     Hybrid Journal   (Followers: 1)
ASHA Leader     Open Access  
Asia Pacific Family Medicine     Open Access   (Followers: 1)
Asia Pacific Journal of Clinical Nutrition     Full-text available via subscription   (Followers: 12)
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: 5)
Asian Journal of Health     Open Access   (Followers: 3)
Asian Journal of Medical and Biological Research     Open Access   (Followers: 4)
Asian Journal of Medical and Pharmaceutical Researches     Open Access   (Followers: 2)
Asian Journal of Medical Sciences     Open Access   (Followers: 2)
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  
Atención Familiar     Open Access  
Atención Primaria     Open Access   (Followers: 1)
Atti della Accademia Peloritana dei Pericolanti - Classe di Scienze Medico-Biologiche     Open Access  
Audiology - Communication Research     Open Access   (Followers: 10)
Auris Nasus Larynx     Full-text available via subscription  
Australian Coeliac     Full-text available via subscription   (Followers: 1)
Australian Family Physician     Full-text available via subscription   (Followers: 3)
Australian Journal of Medical Science     Full-text available via subscription   (Followers: 1)

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Similar Journals
Journal Cover
Artificial Intelligence in Medicine
Journal Prestige (SJR): 0.766
Citation Impact (citeScore): 3
Number of Followers: 17  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0933-3657
Published by Elsevier Homepage  [3181 journals]
  • 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. Sousa MotivationEmergency 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 Hoyle Antibiotic 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. Wong Obstetric 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 Tian The 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
           patients
    • Abstract: Publication date: Available online 16 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Anam Mustaqeem, Syed Muhammad Anwar, Muhammad Majid In 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 Elmaghraby Pressure 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 Car In 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. Tortorella In 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 Palacios BackgroundThe 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
       
  • Motor imagery EEG recognition with KNN-based smooth auto-encoder
    • Abstract: Publication date: Available online 11 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Xianlun Tang, Ting Wang, Yiming Du, Yuyan Dai As new human-computer interaction technology, brain-computer interface has been widely used in various fields of life. The study of EEG signals can not only improve people's awareness of the brain, but also establish new ways for the brain to communicate with the outside world. This paper takes the motion imaging EEG signal as the research object and proposes an innovative semi-supervised model called KNN-based smooth auto-encoder (k-SAE). K-SAE looks for the nearest neighbor values of the samples to construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE). The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature. Besides, the data information and spatial position of the feature map are recorded by max-pooling and unpooling, that help to prevent loss of important information. The method is applied to two data sets for feature extraction and classification experiments of motor imaging EEG signals. The experimental results show that k-SAE achieves good recognition accuracy and outperforms other state-of-the-art recognition algorithms.
       
  • A Novel Chinese Herbal Medicine Clustering Algorithm Via Artificial Bee
           Colony Optimization
    • Abstract: Publication date: Available online 10 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Nan Han, Shaojie Qiao, Guan Yuan, Ping Huang, Dingxiang Liu, Kun Yue Traditional Chinese medicine (TCM) has become popular and been viewed as an effective clinical treatment across the world. Accordingly, there is an ever-increasing interest in performing data analysis over TCM data. Aiming to cope with the problem of excessively depending on empirical values when selecting cluster centers by traditional clustering algorithms, an improved artificial bee colony algorithm is proposed by which to automatically select cluster centers and apply it to aggregate Chinese herbal medicines. The proposed method integrates the following new techniques: (1) improving the artificial bee colony algorithm by applying a new searching strategy of neighbour nectar, (2) employing the improved artificial bee colony algorithm to optimize the parameters of the cutoff distance dc, the local density ρi and the minimum distance δi between the element i and any other element with higher density in the cluster algorithm by fast search and finding of density peaks (called DP algorithm) to find the optimal cluster centers, in order to clustering herbal medicines in an accurate fashion with the guarantee of runtime performance. Extensive experiments were conducted on the UCI benchmark datasets and the TCM datasets and the results verify the effectiveness of the proposed method by comparing it with classical clustering algorithms including K-means, K-mediods and DBSCAN in multiple evaluation metrics, that is, Silhouette Coefficient, Entropy, Purity, Precision, Recall and F1-Measure. The results show that the IABC-DP algorithm outperforms other approaches with good clustering quality and accuracy on the UCI and the TCM datasets as well. In addition, it can be found that the improved artificial bee colony algorithm can effectively reduce the number of iterations when compared to the traditional bee colony algorithm. In particular, the IABC-DP algorithm is applied to cluster multi-dimensional Chinese herbal medicines and the result shows that it outperforms other clustering algorithms in clustering Chinese herbal medicines, which can contribute to a larger effort targeted at advancing the study of discovering composition rules of traditional Chinese prescriptions.
       
  • 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. Diwakaran Individuals 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 Sharma In 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 Badami Early 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 Nair Paralyzed 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.
       
  • Compositional Model based on Factorial Evolution for Realizing Multi-Task
           Learning in Bacterial Virulent Protein Prediction
    • Abstract: Publication date: Available online 7 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Deepak Singh, Pradeep Singh, Dilip Singh Sisodia The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for Multitask Learning (MTL) methods. Identification of Bacterial virulent protein is one such application that helps in understanding the virulence mechanism for the design of drug and vaccine. However, the limiting factor in a reliable prediction model is the scarcity of the experimentally verified training data. To deal with, casting the problem in a Multitask Learning scenario, could be beneficial. Reusability of auxiliary data from related multiple domains in the prediction of target domain with limited labeled data is the primary objective of multitask learning model. Due to the amalgamation of multiple related data, it is possible that the probability distribution between the features tends to vary. Therefore, to deal with change amongst the feature distribution, this paper proposes a composite model for multitask learning framework which is based on two principles: discovering the shared parameters for identifying the relationships between tasks and common underlying representation of features amongst the related tasks. Through multi-kernel and factorial evolution, the proposed framework able to discover the shared kernel parameters and latent feature representation that is common amongst the tasks. To examine the benefits of the proposed model, an extensive experiment is performed on the freely available dataset at VirulentPred web server. Based on the results, we found that multitask learning model performs better than the conventional single task model. Additionally, our findings state that if the distribution between the tasks is high, then training the multiple models yield slightly better prediction. However, if the data distribution difference is low, multitask learning significantly outperforms the individual learning.
       
  • Automated Classification of Histopathology Images Using Transfer Learning
    • Abstract: Publication date: Available online 3 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Muhammed Talo Early and accurate diagnosis of diseases can often save lives. Diagnosis of diseases from tissue samples is done manually by pathologists. Diagnostics process is usually time consuming and expensive. Hence, automated analysis of tissue samples from histopathology images has critical importance for early diagnosis and treatment. The computer aided systems can improve the quality of diagnoses and give pathologists a second opinion for critical cases. In this study, a deep learning based transfer learning approach has been proposed to classify histopathology images automatically. Two well-known and current pre-trained convolutional neural network (CNN) models, ResNet-50 and DenseNet-161, have been trained and tested using color and grayscale images. The DenseNet-161 tested on grayscale images and obtained the best classification accuracy of 97.89%. Additionally, ResNet-50 pre-trained model was tested on the color images of the Kimia Path24 dataset and achieved the highest classification accuracy of 98.87%. According to the obtained results, it may be said that the proposed pre-trained models can be used for fast and accurate classification of histopathology images and assist pathologists in their daily clinical tasks.
       
  • 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 Yu Lung 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.
       
  • The social phenotype: Extracting a patient-centered perspective of
           diabetes from health-related blogs
    • Abstract: Publication date: November 2019Source: Artificial Intelligence in Medicine, Volume 101Author(s): Andrea Lenzi, Marianna Maranghi, Giovanni Stilo, Paola Velardi MotivationsIt has recently been argued [1] that the effectiveness of a cure depends on the doctor–patient shared understanding of an illness and its treatment. Although a better communication between doctor and patient can be pursued through dedicated training programs, or by collecting patients’ experiences and symptoms by means of questionnaires, the impact of these actions is limited by time and resources. In this paper we suggest that a patient-centered view of a disease – as well as potential misalignment between patient and doctor focuses – can be inferred at a larger scale through automated textual analysis of health-related forums. People are generating an enormous amount of social data to describe their health care experiences, and continuously search information about diseases, symptoms, diagnoses, doctors, treatment options and medicines. By automatically collecting, analyzing and exploiting this information, it is possible to obtain a more detailed and nuanced vision of patients’ experience, that we call the “social phenotype” of diseases.Materials and methodsAs a use-case for our analysis, we consider diabetes, a widespread disease in most industrialized countries. We create a high quality data sample of diabetic patients’ messages in Italy, extracted from popular medical forums during more than 10 years. Next, we use a state-of-the-art topic extraction technique based on generative statistical models improved with word embeddings, to identify the main complications, the frequently reported symptoms and the common concerns of these patients. Finally, in order to detect differences in focus, we compare the results of our analysis with available quality of life (QoL) assessments obtained with standard methodologies, such as questionnaires and survey studies.ResultsWe show that patients with diabetes, when accessing on-line forums, express a perception of their disease in a way that might be noticeably different from what is inferred from published QoL assessments on diabetes. In our study, we found that issues reported to have a daily impact on these patients are diet, glycemic control, drugs and clinical tests. These problems are not commonly considered in QoL assessments, since they are not perceived by doctors as representing severe limitations. Although limited to the case of Italian diabetic patients, we suggest that the methodology described in this paper, which is language and disease agnostic, could be applied to other diseases and countries, since misalignment between doctor and patients, and the importance of collecting unbiased patient perceptions, has been emphasized in many studies ([2], [3] inter alia). Extracting the social phenotype of a disease might help acquiring patient-centered information on health care experiences on a much wider scale.
       
  • A hybrid machine learning approach to cerebral stroke prediction based on
           imbalanced medical dataset
    • Abstract: Publication date: Available online 23 October 2019Source: Artificial Intelligence in MedicineAuthor(s): Tianyu Liu, Wenhui Fan, Cheng Wu Background and Objective: Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical staff to decide whether special precautions are needed for a potential patient only based on the monitoring of physiological indicators unless they are obviously abnormal. This paper will develop a hybrid machine learning approach to predict cerebral stroke for clinical diagnosis based on the physiological data with incompleteness and class imbalance.Methods: Two steps are involved in the whole process. Firstly, random forest regression is adopted to impute missing values before classification. Secondly, an automated hyperparameter optimization(AutoHPO) based on deep neural network(DNN) is applied to stroke prediction on an imbalanced dataset.Results: The medical dataset contains 43400 records of potential patients which includes 783 occurrences of stroke. The false negative rate from our prediction approach is only 19.1%, which has reduced by an average of 51.5% in comparison to other traditional approaches. The false positive rate, accuracy and sensitivity predicted by the proposed approach are respectively 33.1%, 71.6%, 67.4%.Conclusion: The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate for stroke prediction. The results are more reliable and valid as the reference in stroke prognosis, and also can be acquired conveniently at a low cost.
       
  • Methods for algorithmic diagnosis of metabolic syndrome
    • Abstract: Publication date: November 2019Source: Artificial Intelligence in Medicine, Volume 101Author(s): Dunja Vrbaški, Milan Vrbaški, Aleksandar Kupusinac, Darko Ivanović, Edita Stokić, Dragan Ivetić, Ksenija Doroslovački Metabolic Syndrome (MetS) is associated with the risk of developing chronic disease (atherosclerotic cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease) and has an important role in early prevention. Previous research showed that an artificial neural network (ANN) is a suitable tool for algorithmic MetS diagnostics, that includes solely non-invasive, low-cost and easily-obtainabled (NI&LC&EO) diagnostic methods. This paper considers using four well-known machine learning methods (linear regression, artificial neural network, decision tree and random forest) for MetS predictions and provides their comparison, in order to induce and facilitate development of appropriate medical software by using these methods. Training, validation and testing are conducted on the large dataset that includes 3000 persons. Input vectors are very simple and contain the following parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures, while the output is MetS diagnosis in true/false form, made in accordance with International Diabetes Federation (IDF). Comparison leads to the conclusion that random forest achieves the highest specificity (SPC=0.9436), sensitivity (SNS=0.9154), positive (PPV=0.9379) and negative (NPV=0.9150) predictive values. Algorithmic diagnosis of MetS could be beneficial in everyday clinical practice since it can easily identify high risk patients.
       
  • Classifying cancer pathology reports with hierarchical self-attention
           networks
    • Abstract: Publication date: November 2019Source: Artificial Intelligence in Medicine, Volume 101Author(s): Shang Gao, John X. Qiu, Mohammed Alawad, Jacob D. Hinkle, Noah Schaefferkoetter, Hong-Jun Yoon, Blair Christian, Paul A. Fearn, Lynne Penberthy, Xiao-Cheng Wu, Linda Coyle, Georgia Tourassi, Arvind Ramanathan We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate performance on a corpus of 374,899 pathology reports obtained from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. Each pathology report is associated with five clinical classification tasks – site, laterality, behavior, histology, and grade. We compare the performance of the HiSAN against other machine learning and deep learning approaches commonly used on medical text data – Naive Bayes, logistic regression, convolutional neural networks, and hierarchical attention networks (the previous state-of-the-art). We show that HiSANs are superior to other machine learning and deep learning text classifiers in both accuracy and macro F-score across all five classification tasks. Compared to the previous state-of-the-art, hierarchical attention networks, HiSANs not only are an order of magnitude faster to train, but also achieve about 1% better relative accuracy and 5% better relative macro F-score.
       
  • Study on miR-384-5p activates TGF-β signaling pathway to promote neuronal
           damage in abutment nucleus of rats based on deep learning
    • Abstract: Publication date: Available online 10 October 2019Source: Artificial Intelligence in MedicineAuthor(s): Zhen Wang, Xiaoyan Du, Yang Yang, Guoqing Zhang BackgroundAny ailment in our organs can be visualized by using different modality signals and images. Hospitals are encountering a massive influx of large multimodality patient data to be analysed accurately and with context understanding. The deep learning techniques, like convolution neural networks (CNN), long short-term memory (LSTM), autoencoders, deep generative models and deep belief networks have already been applied to efficiently analyse possible large collections of data. Application of these methods to medical signals and images can aid the clinicians in clinical decision making.PurposeThe aim of this study was to explore its potential application mechanism to the abalone basal ganglia neurons in rats based on deep learning.Patients and methodsFirstly, in the GEO database, we obtained data on rat anesthesia, performing differential analysis, co-expression analysis, and enrichment analysis, and then we received the relevant module genes. Besides, the potential regulation of multi-factors on the module was calculated by hypergeometric test, and a series of ncRNA and TF were identified. Finally, we screened the target genes of anesthetized rats to gain insight into the potential role of anesthesia in rat basal lateral nucleus neurons.ResultsA total of 535 differentially expressed genes in rats were obtained, involving Mafb and Ryr2. These genes are clustered into 17 anesthesia-related expression disorder modules. At the same time, the biological processes favored by the module are regulation of neuron apoptotic process and transforming growth factor beta2 production. Pivot analysis found that 39 ncRNAs and 4 TFs drive anesthesia-related disorders. Finally, the mechanism of action was analyzed and predicted. The module was regulated by Acvr1. We believe that miR-384-5p in anesthetized rats can activate the TGF-beta signaling pathway. Further, it promotes anesthesia and causes exposure to the basal ganglia neuron damage of the amygdala.ConclusionIn this study, the imbalance module was used to explore the multi-factor-mediated anesthesia application mechanism, which provided new methods and ideas for subsequent research. The results suggest that miR-384-5p can promote anesthesia damage to the abalone basal ganglia neurons in rats through a variety of biological processes and signaling pathways. This result lays a solid theoretical foundation for biologists to explore the application mechanism of anesthesiology further.
       
  • Cosine Similarity Measures of Bipolar Neutrosophic Set for Diagnosis of
           Bipolar Disorder Diseases
    • Abstract: Publication date: Available online 5 October 2019Source: Artificial Intelligence in MedicineAuthor(s): Mohamed Abdel-Basset, Mai Mohamed, Mohamed Elhoseny, Le Hoang Son, Francisco Chiclana, Abd El-Nasser H. Zaied Similarity plays a significant implicit or explicit role in various fields. In some real applications in decision making, similarity may bring counterintuitive outcomes from the decision maker’s standpoint. Therefore, in this research, we propose some novel similarity measures for bipolar and interval-valued bipolar neutrosophic set such as the cosine similarity measures and weighted cosine similarity measures. The propositions of these similarity measures are examined, and two multi-attribute decision making techniques are presented based on proposed measures. For verifying the feasibility of proposed measures, two numerical examples are presented in comparison with the related methods for demonstrating the practicality of the proposed method. Finally, we applied the proposed measures of similarity for diagnosing bipolar disorder diseases.
       
  • Neural network methodology for real-time modelling of bio-heat transfer
           during thermo-therapeutic applications
    • Abstract: Publication date: Available online 30 September 2019Source: Artificial Intelligence in MedicineAuthor(s): Jinao Zhang, Sunita Chauhan Real-time simulation of bio-heat transfer can improve surgical feedback in thermo-therapeutic treatment, leading to technical innovations to surgical process and improvements to patient outcomes; however, it is challenging to achieve real-time computational performance by conventional methods. This paper presents a cellular neural network (CNN) methodology for fast and real-time modelling of bio-heat transfer with medical applications in thermo-therapeutic treatment. It formulates nonlinear dynamics of the bio-heat transfer process and spatially discretised bio-heat transfer equation as the nonlinear neural dynamics and local neural connectivity of CNN, respectively. The proposed CNN methodology considers three-dimensional (3-D) volumetric bio-heat transfer behaviour in tissue and applies the concept of control volumes for discretisation of the Pennes bio-heat transfer equation on 3-D irregular grids, leading to novel neural network models embedded with bio-heat transfer mechanism for computation of tissue temperatures and associated thermal dose. Simulations and comparative analyses demonstrate that the proposed CNN models can achieve good agreement with the commercial finite element analysis package, ABAQUS/CAE, in numerical accuracy and reduce computation time by 304 and 772.86 times compared to those of with and without ABAQUS parallel execution, far exceeding the computational performance of the commercial finite element codes. The medical application is demonstrated using a high-intensity focused ultrasound (HIFU)-based thermal ablation of hepatic cancer for prediction of tissue temperatures and estimation of thermal dose.
       
  • Multi-criterion mammographic risk analysis supported with multi-label
           fuzzy-rough feature selection
    • Abstract: Publication date: September 2019Source: Artificial Intelligence in Medicine, Volume 100Author(s): Yanpeng Qu, Guanli Yue, Changjing Shang, Longzhi Yang, Reyer Zwiggelaar, Qiang Shen Context and backgroundBreast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution.MotivationComputer aided risk analysis of breast cancer typically works with a set of extracted mammographic features which may contain significant redundancy and noise, thereby requiring technical developments to improve runtime performance in both computational efficiency and classification accuracy.HypothesisUse of advanced feature selection methods based on multiple diagnosis criteria may lead to improved results for mammographic risk analysis.MethodsAn approach for multi-criterion based mammographic risk analysis is proposed, by adapting the recently developed multi-label fuzzy-rough feature selection mechanism.ResultsA system for multi-criterion mammographic risk analysis is implemented with the aid of multi-label fuzzy-rough feature selection and its performance is positively verified experimentally, in comparison with representative popular mechanisms.ConclusionsThe novel approach for mammographic risk analysis based on multiple criteria helps improve classification accuracy using selected informative features, without suffering from the redundancy caused by such complex criteria, with the implemented system demonstrating practical efficacy.
       
  • Leveraging implicit expert knowledge for non-circular machine learning in
           sepsis prediction
    • Abstract: Publication date: September 2019Source: Artificial Intelligence in Medicine, Volume 100Author(s): Shigehiko Schamoni, Holger A. Lindner, Verena Schneider-Lindner, Manfred Thiel, Stefan Riezler Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians’ daily judgements of patients’ sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.
       
  • Automated plaque classification using computed tomography angiography and
           Gabor transformations
    • Abstract: Publication date: Available online 14 September 2019Source: Artificial Intelligence in MedicineAuthor(s): U. Rajendra Acharya, Kristen M. Meiburger, Joel En Wei Koh, Jahmunah Vicnesh, Edward J. Ciaccio, Oh Shu Lih, Sock Keow Tan, Raja Rizal Azman Raja Aman, Filippo Molinari, Kwan Hoong Ng Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients. The automated technique is based on various features that are extracted from the Gabor transform of the acquired CTA images. Specifically, seven features are extracted from the Gabor coefficients : energy, and Kapur, Max, Rényi, Shannon, Vajda, and Yager entropies. The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. Moreover, two well-known feature reduction techniques were employed, and the features acquired were also ranked according to F-value and input to several classifiers. The best classification results were obtained using all computed features without the employment of feature reduction, using a probabilistic neural network. An accuracy, positive predictive value, sensitivity, and specificity of 89.09%, 91.70%, 91.83% and 83.70% was obtained, respectively. Based on these results, it is evident that the technique can be helpful in the automated classification of plaques present in CTA images, and may become an important tool to reduce procedural costs and patient radiation dose. This could also aid clinicians in plaque diagnostics.
       
  • 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 Zhang Epilepsy 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.
       
  • A Novel Model for Evaluation Hospital Medical Care Systems Based on
           Plithogenic Sets
    • Abstract: Publication date: Available online 31 August 2019Source: Artificial Intelligence in MedicineAuthor(s): Mohamed Abdel-Basset, Mohamed El-hoseny, Abduallah Gamal, Florentin Smarandache This research suggests an approach constructed on the connotation of plithogenic theory and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) technique to come up with a methodical procedure to assess the infirmary serving under a framework of plithogenic theory, where the ambiguity, incomplete information, qualitative information, approximate evaluation, imprecision and uncertainty are addressed with semantic expressions determined by plithogenic numbers and computing of contradiction degrees of attribute values. This research stratifies the plithogenic multi criteria decision making (MCDM) strategy for defining the significant weights of assessing standards, and the VIKOR technique is applied for enhancing the serving efficiency classifications of the possible substitutes. An experimental issue, including 11 assessing standards, 3 private and 2 general hospitals in Zagazig, has been evaluated by 3 assessors from several areas of medical activities, asked to validate the suggested strategy. In this research, we give some definitions of the plithogenic environment, which is more general and comprehensive than fuzzy, intuitionistic fuzzy and neutrosophic ones. The plithogeny is interested in the contradiction degrees between attribute values that help in better calculating the aggregations. We conducted the data analysis and the results showed us that the serving efficiency of private medical centers is superior than that of general medical centers due to the fact that public medical centers are scarcely supported by governmental institutions. The private medical centers have to ward themselves to keep possession of bringing patients or attract patients. We conducted the sensitivity analysis of the achieved results, to verify their validity, and to find out to what extent the different values affect the ranking of available alternatives.
       
  • Prediction of Progression in Idiopathic Pulmonary Fibrosis using CT Scans
           at Baseline: A Quantum Particle Swarm Optimization - Random Forest
           Approach
    • Abstract: Publication date: Available online 28 August 2019Source: Artificial Intelligence in MedicineAuthor(s): Yu Shi, Weng Kee Wong, Jonathan G. Goldin, Matthew S. Brown, Grace Hyun J. Kim Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, there are two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans and their follow-up status; and (b) simultaneously selecting important features from high-dimensional space, and optimizing the prediction performance. We resolved the first challenge by implementing a study design and having an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-up visits. For the second challenge, we integrated the feature selection with prediction by developing an algorithm using a wrapper method that combines quantum particle swarm optimization to select a small number of features with random forest to classify early patterns of progression. We applied our proposed algorithm to analyze anonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields a parsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROI level. These results are superior to other popular feature selections and classification methods, in that our method produces higher accuracy in prediction of progression and more balanced sensitivity and specificity with a smaller number of selected features. Our work is the first approach to show that it is possible to use only baseline HRCT scans to predict progressive ROIs at 6 months to 1 year follow-ups using artificial intelligence.
       
  • The Virtual Doctor: An Interactive Clinical-Decision-Support System based
           on Deep Learning for Non-Invasive Prediction of Diabetes
    • Abstract: Publication date: Available online 21 August 2019Source: Artificial Intelligence in MedicineAuthor(s): Sebastian Spänig, Agnes Emberger-Klein, Jan-Peter Sowa, Ali Canbay, Klaus Menrad, Dominik Heider Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction.In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks. Moreover, the system provides an easy-to-interpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such a system in the future.
       
  • The Role of Medical Smartphone Apps in Clinical Decision-Support: A
           Literature Review
    • Abstract: Publication date: Available online 21 August 2019Source: Artificial Intelligence in MedicineAuthor(s): Helena A. Watson, Rachel M. Tribe, Andrew H. Shennan IntroductionThe now ubiquitous smartphone has huge potential to assist clinical decision-making across the globe. However, the rapid pace of digitalisation contrasts starkly with the slower rate of medical research and publication. This review explores the evidence base that exists to validate and evaluate the use of medical decision-support apps. The resultant findings will inform appropriate and pragmatic evaluation strategies for future clinical app developers and provide a scientific and cultural context for research priorities in this field.MethodMedline, Embase and Cochrane databases were searched for clinical trials concerning decision support and smart phones from 2007 (introduction of first smartphone iPhone) until January 2019.ResultsFollowing exclusions, 48 trials and one Cochrane review were included for final analysis. Whilst diagnostic accuracy studies are plentiful, clinical trials are scarce. App research methodology was further interrogated according to setting and decision-support modality: e.g. camera-based, guideline-based, predictive models. Description of app development pathways and regulation were highly varied. Global health emerged as an early adopter of decision-support apps and this field is leading implementation and evaluation.ConclusionClinical decision-support apps have considerable potential to enhance access to care and quality of care, but the medical community must rise to the challenge of modernising its approach if it is truly committed to capitalising on the opportunities of digitalisation.
       
  • On strategic choices faced by large pharmaceutical laboratories and their
           effect on innovation risk under fuzzy conditions
    • Abstract: Publication date: Available online 11 August 2019Source: Artificial Intelligence in MedicineAuthor(s): Javier Puente ObjectivesWe develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FIS), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of innovation risk.Methods and MaterialsThree FIS, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to the strategic choices they face. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base were agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and validated the model.ResultsIn an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret the results. We have also run regressions to better understand the implications of our results.ConclusionsThe main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of the system, which may be easily applied to other settings.
       
  • Automated detection of schizophrenia using nonlinear signal processing
           methods
    • Abstract: Publication date: Available online 20 July 2019Source: Artificial Intelligence in MedicineAuthor(s): V. Jahmunah, Shu Lih Oh, Rajinikanth V, Edward J. Ciaccio, Kang Hao Cheong, Arunkumar, U. Rajendra Acharya Examination of the brain’s condition with the Electroencephalogram (EEG) can be helpful to predict abnormality and cerebral activities. The purpose of this study was to develop an Automated Diagnostic Tool (ADT) to investigate and classify the EEG signal patterns into normal and schizophrenia classes. The ADT implements a sequence of events, such as EEG series splitting, non-linear features mining, t-test assisted feature selection, classification and validation. The proposed ADT is employed to evaluate a 19-channel EEG signal collected from normal and schizophrenia class volunteers. A dataset was created by splitting the raw 19-channel EEG into a sequence of 6250 sample points, which was helpful to produce 1142 features of normal and schizophrenia class patterns. Non-linear feature extraction was then implemented to mine 157 features from each EEG pattern, from which 14 of the principal features were identified based on significance. Finally, a signal classification practice with Decision-Tree (DT), Linear-Discriminant analysis (LD), k-Nearest-Neighbour (KNN), Probabilistic-Neural-Network (PNN), and Support-Vector-Machine (SVM) with various kernels was implemented. The experimental outcome showed that the SVM with Radial-Basis-Function (SVM-RBF) offered a superior average performance value of 92.91% on the considered EEG dataset, as compared to other classifiers implemented in this work.
       
 
 
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