Subjects -> HEALTH AND SAFETY (Total: 1541 journals)
    - CIVIL DEFENSE (22 journals)
    - DRUG ABUSE AND ALCOHOLISM (86 journals)
    - HEALTH AND SAFETY (722 journals)
    - HEALTH FACILITIES AND ADMINISTRATION (390 journals)
    - OCCUPATIONAL HEALTH AND SAFETY (108 journals)
    - PHYSICAL FITNESS AND HYGIENE (131 journals)
    - WOMEN'S HEALTH (82 journals)

HEALTH AND SAFETY (722 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 203 Journals sorted alphabetically
16 de Abril     Open Access   (Followers: 4)
Acta Informatica Medica     Open Access   (Followers: 2)
Acta Scientiarum. Health Sciences     Open Access   (Followers: 3)
Adultspan Journal     Hybrid Journal   (Followers: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 12)
Advances in Public Health     Open Access   (Followers: 28)
Adversity and Resilience Science : Journal of Research and Practice     Hybrid Journal   (Followers: 3)
African Health Sciences     Open Access   (Followers: 5)
African Journal for Physical, Health Education, Recreation and Dance     Full-text available via subscription   (Followers: 7)
African Journal of Health Professions Education     Open Access   (Followers: 6)
Afrimedic Journal     Open Access   (Followers: 3)
Ageing & Society     Hybrid Journal   (Followers: 47)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 7)
AJOB Empirical Bioethics     Hybrid Journal   (Followers: 3)
Akademika     Open Access   (Followers: 1)
American Journal of Family Therapy     Hybrid Journal   (Followers: 10)
American Journal of Health Economics     Full-text available via subscription   (Followers: 21)
American Journal of Health Education     Hybrid Journal   (Followers: 36)
American Journal of Health Promotion     Hybrid Journal   (Followers: 34)
American Journal of Health Sciences     Open Access   (Followers: 12)
American Journal of Health Studies     Full-text available via subscription   (Followers: 16)
American Journal of Preventive Medicine     Hybrid Journal   (Followers: 32)
American Journal of Public Health     Full-text available via subscription   (Followers: 280)
American Journal of Public Health Research     Open Access   (Followers: 28)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 6)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 8)
Annales des Sciences de la Santé     Open Access  
Annali dell'Istituto Superiore di Sanità     Open Access  
Annals of Global Health     Open Access   (Followers: 14)
Annals of Health Law     Open Access   (Followers: 6)
Annals of Tropical Medicine and Public Health     Open Access   (Followers: 15)
Applied Biosafety     Hybrid Journal   (Followers: 1)
Applied Research In Health And Social Sciences: Interface And Interaction     Open Access   (Followers: 5)
Apuntes Universitarios     Open Access   (Followers: 1)
Archive of Community Health     Open Access   (Followers: 1)
Archives of Community Medicine and Public Health     Open Access   (Followers: 2)
Archives of Medicine and Health Sciences     Open Access   (Followers: 5)
Archives of Suicide Research     Hybrid Journal   (Followers: 10)
Archivos de Prevención de Riesgos Laborales     Open Access   (Followers: 1)
Arquivos de Ciências da Saúde     Open Access  
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 11)
Asia Pacific Journal of Health Management     Full-text available via subscription   (Followers: 5)
Asia-Pacific Journal of Public Health     Hybrid Journal   (Followers: 12)
Asian Journal of Gambling Issues and Public Health     Open Access   (Followers: 4)
Asian Journal of Medicine and Health     Open Access   (Followers: 1)
Atención Primaria     Open Access   (Followers: 2)
Atención Primaria Práctica     Open Access   (Followers: 1)
Australasian Journal of Paramedicine     Open Access   (Followers: 6)
Australian Advanced Aesthetics     Full-text available via subscription   (Followers: 4)
Australian Family Physician     Full-text available via subscription   (Followers: 3)
Australian Indigenous HealthBulletin     Free   (Followers: 5)
Autism & Developmental Language Impairments     Open Access   (Followers: 16)
Behavioral Healthcare     Full-text available via subscription   (Followers: 8)
Bijzijn     Hybrid Journal   (Followers: 1)
Bijzijn XL     Hybrid Journal  
Biomedical Safety & Standards     Full-text available via subscription   (Followers: 8)
Biosafety and Health     Open Access   (Followers: 5)
Biosalud     Open Access   (Followers: 1)
Birat Journal of Health Sciences     Open Access  
BLDE University Journal of Health Sciences     Open Access  
BMC Oral Health     Open Access   (Followers: 7)
BMC Pregnancy and Childbirth     Open Access   (Followers: 24)
BMJ Simulation & Technology Enhanced Learning     Hybrid Journal   (Followers: 12)
Boletin Médico de Postgrado     Open Access  
Brazilian Journal of Medicine and Human Health     Open Access  
British Journal of Health Psychology     Hybrid Journal   (Followers: 50)
Buletin Penelitian Kesehatan     Open Access   (Followers: 2)
Buletin Penelitian Sistem Kesehatan     Open Access  
Bulletin of the World Health Organization     Open Access   (Followers: 23)
Cadernos de Educação, Saúde e Fisioterapia     Open Access   (Followers: 1)
Cadernos de Saúde     Open Access   (Followers: 1)
Cadernos Saúde Coletiva     Open Access   (Followers: 1)
Cambridge Quarterly of Healthcare Ethics     Hybrid Journal   (Followers: 14)
Canadian Family Physician     Partially Free   (Followers: 13)
Canadian Journal of Community Mental Health     Full-text available via subscription   (Followers: 14)
Canadian Journal of Human Sexuality     Hybrid Journal   (Followers: 3)
Canadian Journal of Public Health     Hybrid Journal   (Followers: 27)
Cannabis and Cannabinoid Research     Hybrid Journal   (Followers: 2)
Carta Comunitaria     Open Access  
Case Reports in Women's Health     Open Access   (Followers: 4)
Case Studies in Fire Safety     Open Access   (Followers: 25)
CASUS : Revista de Investigación y Casos en Salud     Open Access   (Followers: 1)
Central Asian Journal of Global Health     Open Access   (Followers: 2)
CES Medicina     Open Access  
CES Salud Pública     Open Access   (Followers: 1)
Child Abuse Research in South Africa     Full-text available via subscription   (Followers: 1)
Child's Nervous System     Hybrid Journal  
Childhood Obesity and Nutrition     Open Access   (Followers: 11)
Children     Open Access   (Followers: 2)
CHRISMED Journal of Health and Research     Open Access   (Followers: 2)
Christian Journal for Global Health     Open Access  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
Ciencia & Salud     Open Access   (Followers: 2)
Ciencia & Trabajo     Open Access   (Followers: 1)
Ciencia e Innovación en Salud     Open Access  
Ciencia y Cuidado     Open Access   (Followers: 1)
Ciencia y Salud     Open Access   (Followers: 4)
Ciencia y Salud Virtual     Open Access  
Ciencia, Tecnología y Salud     Open Access   (Followers: 2)
Cities & Health     Hybrid Journal   (Followers: 2)
Clinical and Experimental Health Sciences     Open Access   (Followers: 1)
ClinicoEconomics and Outcomes Research     Open Access   (Followers: 2)
Clocks & Sleep     Open Access   (Followers: 2)
CME     Hybrid Journal   (Followers: 2)
CoDAS     Open Access  
Community Health     Open Access   (Followers: 6)
Conflict and Health     Open Access   (Followers: 8)
Contraception and Reproductive Medicine     Open Access   (Followers: 2)
Cuaderno de investigaciones: semilleros andina     Open Access   (Followers: 3)
Cuadernos de la Escuela de Salud Pública     Open Access  
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 10)
Current Opinion in Environmental Science & Health     Hybrid Journal   (Followers: 1)
Das österreichische Gesundheitswesen ÖKZ     Hybrid Journal  
Day Surgery Australia     Full-text available via subscription   (Followers: 2)
Design for Health     Hybrid Journal  
Digital Health     Open Access   (Followers: 9)
Disaster Medicine and Public Health Preparedness     Hybrid Journal   (Followers: 14)
Diversity and Equality in Health and Care     Open Access   (Followers: 9)
Diversity of Research in Health Journal     Open Access  
Dramatherapy     Hybrid Journal   (Followers: 3)
Drogues, santé et société     Open Access   (Followers: 2)
Duazary     Open Access   (Followers: 1)
Düzce Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi / Journal of Duzce University Health Sciences Institute     Open Access  
Early Childhood Research Quarterly     Hybrid Journal   (Followers: 26)
East African Journal of Public Health     Full-text available via subscription   (Followers: 4)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 24)
EcoHealth     Hybrid Journal   (Followers: 5)
Education for Health     Open Access   (Followers: 9)
ElectronicHealthcare     Full-text available via subscription   (Followers: 3)
Elsevier Ergonomics Book Series     Full-text available via subscription   (Followers: 5)
Emergency Services SA     Full-text available via subscription   (Followers: 2)
Ensaios e Ciência : Ciências Biológicas, Agrárias e da Saúde     Open Access  
Environmental Disease     Open Access   (Followers: 4)
Environmental Sciences Europe     Open Access   (Followers: 2)
Epidemics     Open Access   (Followers: 5)
Epidemiologic Perspectives & Innovations     Open Access   (Followers: 6)
Epidemiology, Biostatistics and Public Health     Open Access   (Followers: 22)
EsSEX : Revista Científica     Open Access   (Followers: 1)
Estudios sociales : Revista de alimentación contemporánea y desarrollo regional     Open Access   (Followers: 1)
Ethics & Human Research     Hybrid Journal   (Followers: 3)
Ethics, Medicine and Public Health     Full-text available via subscription   (Followers: 7)
Ethiopian Journal of Health Development     Open Access   (Followers: 7)
Ethiopian Journal of Health Sciences     Open Access   (Followers: 8)
Ethnicity & Health     Hybrid Journal   (Followers: 14)
Eurasian Journal of Health Technology Assessment     Open Access  
EUREKA : Health Sciences     Open Access   (Followers: 2)
European Journal of Investigation in Health, Psychology and Education     Open Access   (Followers: 5)
European Medical, Health and Pharmaceutical Journal     Open Access   (Followers: 1)
Evaluation & the Health Professions     Hybrid Journal   (Followers: 10)
Evidence-based Medicine & Public Health     Open Access   (Followers: 9)
Evidência - Ciência e Biotecnologia - Interdisciplinar     Open Access  
Expressa Extensão     Open Access  
Face à face     Open Access   (Followers: 1)
Families, Systems, & Health     Full-text available via subscription   (Followers: 9)
Family & Community Health     Hybrid Journal   (Followers: 14)
Family Medicine and Community Health     Open Access   (Followers: 10)
Family Relations     Partially Free   (Followers: 15)
Fatigue : Biomedicine, Health & Behavior     Hybrid Journal   (Followers: 2)
Finnish Journal of eHealth and eWelfare : Finjehew     Open Access  
Food and Public Health     Open Access   (Followers: 19)
Food Quality and Safety     Open Access   (Followers: 1)
Frontiers in Digital Health     Open Access   (Followers: 2)
Frontiers in Public Health     Open Access   (Followers: 9)
Frontiers of Health Services Management     Partially Free   (Followers: 4)
Gaceta Sanitaria     Open Access   (Followers: 3)
Galen Medical Journal     Open Access   (Followers: 1)
Ganesha Journal     Open Access  
Gazi Sağlık Bilimleri Dergisi     Open Access  
Geospatial Health     Open Access   (Followers: 1)
Gestão e Desenvolvimento     Open Access  
Gesundheitsökonomie & Qualitätsmanagement     Hybrid Journal   (Followers: 9)
Giornale Italiano di Health Technology Assessment     Full-text available via subscription  
Global Advances in Health and Medicine     Open Access  
Global Challenges     Open Access  
Global Health : Science and Practice     Open Access   (Followers: 8)
Global Health Annual Review     Open Access   (Followers: 5)
Global Health Journal     Open Access   (Followers: 2)
Global Health Promotion     Hybrid Journal   (Followers: 17)
Global Journal of Health Science     Open Access   (Followers: 10)
Global Journal of Public Health     Open Access   (Followers: 14)
Global Medical & Health Communication     Open Access   (Followers: 2)
Global Mental Health     Open Access   (Followers: 9)
Global Reproductive Health     Open Access   (Followers: 1)
Global Security : Health, Science and Policy     Open Access   (Followers: 1)
Global Transitions     Open Access   (Followers: 1)
Globalization and Health     Open Access   (Followers: 9)
Hacia la Promoción de la Salud     Open Access  
Hastane Öncesi Dergisi     Open Access  
Hastings Center Report     Hybrid Journal   (Followers: 5)
HCU Journal     Open Access  
HEADline     Hybrid Journal  
Health & Place     Hybrid Journal   (Followers: 19)
Health & Justice     Open Access   (Followers: 6)
Health : An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine     Hybrid Journal   (Followers: 15)
Health and Human Rights     Open Access   (Followers: 10)
Health and Research Journal     Open Access   (Followers: 4)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 11)
Health and Social Work     Hybrid Journal   (Followers: 71)
Health Behavior and Policy Review     Full-text available via subscription   (Followers: 5)
Health Behavior Research     Open Access   (Followers: 6)

        1 2 3 4 | Last

Similar Journals
Journal Cover
Frontiers in Digital Health
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2673-253X
Published by Frontiers Media Homepage  [86 journals]
  • An Effective Multimodal Image Fusion Method Using MRI and PET for
           Alzheimer's Disease Diagnosis

    • Authors: Juan Song, Jian Zheng, Ping Li, Xiaoyuan Lu, Guangming Zhu, Peiyi Shen
      Abstract: Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called “GM-PET.” The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.
      PubDate: 2021-02-26T00:00:00Z
       
  • Editorial: Coaching Systems for Health and Well-Being

    • Authors: Evdokimos I. Konstantinidis, Eleftheria Vellidou, Luis Fernandez-Luque, Panagiotis D. Bamidis
      PubDate: 2021-02-25T00:00:00Z
       
  • Identifying Heart Failure in ECG Data With Artificial Intelligence—A
           Meta-Analysis

    • Authors: Dimitri Grün, Felix Rudolph, Nils Gumpfer, Jennifer Hannig, Laura K. Elsner, Beatrice von Jeinsen, Christian W. Hamm, Andreas Rieth, Michael Guckert, Till Keller
      Abstract: Introduction: Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF). Artificial intelligence (AI) can be used for semi-automated ECG analysis. The aim of this evaluation was to provide an overview of AI use in HF detection from ECG signals and to perform a meta-analysis of available studies.Methods and Results: An independent comprehensive search of the PubMed and Google Scholar database was conducted for articles dealing with the ability of AI to predict HF based on ECG signals. Only original articles published in peer-reviewed journals were considered. A total of five reports including 57,027 patients and 579,134 ECG datasets were identified including two sets of patient-level data and three with ECG-based datasets. The AI-processed ECG data yielded areas under the receiver operator characteristics curves between 0.92 and 0.99 to identify HF with higher values in ECG-based datasets. Applying a random-effects model, an sROC of 0.987 was calculated. Using the contingency tables led to diagnostic odds ratios ranging from 3.44 [95% confidence interval (CI) = 3.12–3.76] to 13.61 (95% CI = 13.14–14.08) also with lower values in patient-level datasets. The meta-analysis diagnostic odds ratio was 7.59 (95% CI = 5.85–9.34).Conclusions: The present meta-analysis confirms the ability of AI to predict HF from standard 12-lead ECG signals underlining the potential of such an approach. The observed overestimation of the diagnostic ability in artificial ECG databases compared to patient-level data stipulate the need for robust prospective studies.
      PubDate: 2021-02-25T00:00:00Z
       
  • Trends in Heart-Rate Variability Signal Analysis

    • Authors: Syem Ishaque, Naimul Khan, Sri Krishnan
      Abstract: Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.
      PubDate: 2021-02-25T00:00:00Z
       
  • “Sharing Is Caring:” Australian Self-Trackers' Concepts and Practices
           of Personal Data Sharing and Privacy

    • Authors: Deborah Lupton
      Abstract: Self-tracking technologies and practices offer ways of generating vast reams of personal details, raising questions about how these data are revealed or exposed to others. In this article, I report on findings from an interview-based study of long-term Australian self-trackers who were collecting and reviewing personal information about their bodies and other aspects of their everyday lives. The discussion focuses on the participants' understandings and practices related to sharing their personal data and to data privacy. The contextual elements of self-tracked sharing and privacy concerns were evident in the participants' accounts and were strongly related to ideas about why and how these details should be accessed by others. Sharing personal information from self-tracking was largely viewed as an intimate social experience. The value of self-tracked data to contribute to close face-to-face relationships was recognized and related aspects of social privacy were identified. However, most participants did not consider the possibilities that their personal information could be distributed well-beyond these relationships by third parties for commercial purposes (or what has been termed “institutional privacy”). These findings contribute to a more-than-digital approach to personal data sharing and privacy practices that recognizes the interplay between digital and non-digital practices and contexts. They also highlight the relational and social dimensions of self-tracking and concepts of data privacy.
      PubDate: 2021-02-23T00:00:00Z
       
  • Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment
           in the Intelligent ICU

    • Authors: Benjamin Shickel, Anis Davoudi, Tezcan Ozrazgat-Baslanti, Matthew Ruppert, Azra Bihorac, Parisa Rashidi
      Abstract: Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.
      PubDate: 2021-02-22T00:00:00Z
       
  • Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided
           Neuronavigation Systems

    • Authors: Fotis Drakopoulos, Christos Tsolakis, Angelos Angelopoulos, Yixun Liu, Chengjun Yao, Kyriaki Rafailia Kavazidi, Nikolaos Foroglou, Andrey Fedorov, Sarah Frisken, Ron Kikinis, Alexandra Golby, Nikos Chrisochoides
      Abstract: Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT.Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon.Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in
      PubDate: 2021-02-18T00:00:00Z
       
  • A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine
           Contours

    • Authors: Ping Li, Xiangwen Kong, Johann Li, Guangming Zhu, Xiaoyuan Lu, Peiyi Shen, Syed Afaq Ali Shah, Mohammed Bennamoun, Tao Hua
      Abstract: Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.
      PubDate: 2021-02-17T00:00:00Z
       
  • Utilizing Text Mining, Data Linkage and Deep Learning in Police and Health
           Records to Predict Future Offenses in Family and Domestic Violence

    • Authors: George Karystianis, Rina Carines Cabral, Soyeon Caren Han, Josiah Poon, Tony Butler
      Abstract: Family and Domestic violence (FDV) is a global problem with significant social, economic, and health consequences for victims including increased health care costs, mental trauma, and social stigmatization. In Australia, the estimated annual cost of FDV is $22 billion, with one woman being murdered by a current or former partner every week. Despite this, tools that can predict future FDV based on the features of the person of interest (POI) and victim are lacking. The New South Wales Police Force attends thousands of FDV events each year and records details as fixed fields (e.g., demographic information for individuals involved in the event) and as text narratives which describe abuse types, victim injuries, threats, including the mental health status for POIs and victims. This information within the narratives is mostly untapped for research and reporting purposes. After applying a text mining methodology to extract information from 492,393 FDV event narratives (abuse types, victim injuries, mental illness mentions), we linked these characteristics with the respective fixed fields and with actual mental health diagnoses obtained from the NSW Ministry of Health for the same cohort to form a comprehensive FDV dataset. These data were input into five deep learning models (MLP, LSTM, Bi-LSTM, Bi-GRU, BERT) to predict three FDV offense types (“hands-on,” “hands-off,” “Apprehended Domestic Violence Order (ADVO) breach”). The transformer model with BERT embeddings returned the best performance (69.00% accuracy; 66.76% ROC) for “ADVO breach” in a multilabel classification setup while the binary classification setup generated similar results. “Hands-off” offenses proved the hardest offense type to predict (60.72% accuracy; 57.86% ROC using BERT) but showed potential to improve with fine-tuning of binary classification setups. “Hands-on” offenses benefitted least from the contextual information gained through BERT embeddings in which MLP with categorical embeddings outperformed it in three out of four metrics (65.95% accuracy; 78.03% F1-score; 70.00% precision). The encouraging results indicate that future FDV offenses can be predicted using deep learning on a large corpus of police and health data. Incorporating additional data sources will likely increase the performance which can assist those working on FDV and law enforcement to improve outcomes and better manage FDV events.
      PubDate: 2021-02-17T00:00:00Z
       
  • Spontaneously Generated Online Patient Experience of Modafinil: A
           Qualitative and NLP Analysis

    • Authors: Julia Walsh, Jonathan Cave, Frances Griffiths
      Abstract: Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil.Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results.Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68% positive, 12% mixed, 18% negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72% positive sentiment 4% neutral 24% negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2% of posts. If we allow for one category difference matching was accurate in 85% of posts.Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.
      PubDate: 2021-02-17T00:00:00Z
       
  • How to Develop and Implement a Computerized Decision Support System
           Integrated for Antimicrobial Stewardship' Experiences From Two Swiss
           Hospital Systems

    • Authors: Gaud Catho, Nicolo S. Centemero, Brigitte Waldispühl Suter, Nathalie Vernaz, Javier Portela, Serge Da Silva, Roberta Valotti, Valentina Coray, Francesco Pagnamenta, Alice Ranzani, Marie-Françoise Piuz, Luigia Elzi, Rodolphe Meyer, Enos Bernasconi, Benedikt D. Huttner, The COMPASS Study Group
      Abstract: Background: Computerized decision support systems (CDSS) provide new opportunities for automating antimicrobial stewardship (AMS) interventions and integrating them in routine healthcare. CDSS are recommended as part of AMS programs by international guidelines but few have been implemented so far. In the context of the publicly funded COMPuterized Antibiotic Stewardship Study (COMPASS), we developed and implemented two CDSSs for antimicrobial prescriptions integrated into the in-house electronic health records of two public hospitals in Switzerland. Developing and implementing such systems was a unique opportunity for learning during which we faced several challenges. In this narrative review we describe key lessons learned.Recommendations: (1) During the initial planning and development stage, start by drafting the CDSS as an algorithm and use a standardized format to communicate clearly the desired functionalities of the tool to all stakeholders. (2) Set up a multidisciplinary team bringing together Information Technologies (IT) specialists with development expertise, clinicians familiar with “real-life” processes in the wards and if possible, involve collaborators having knowledge in both areas. (3) When designing the CDSS, make the underlying decision-making process transparent for physicians and start simple and make sure to find the right balance between force and persuasion to ensure adoption by end-users. (4) Correctly assess the clinical and economic impact of your tool, therefore try to use standardized terminologies and limit the use of free text for analysis purpose. (5) At the implementation stage, plan usability testing early, develop an appropriate training plan suitable to end users' skills and time-constraints and think ahead of additional challenges related to the study design that may occur (such as a cluster randomized trial). Stay also tuned to react quickly during the intervention phase. (6) Finally, during the assessment stage plan ahead maintenance, adaptation and related financial challenges and stay connected with institutional partners to leverage potential synergies with other informatics projects.
      PubDate: 2021-02-16T00:00:00Z
       
  • HPV Vaccination Champions: Evaluating a Technology-Mediated Intervention
           for Parents

    • Authors: Beth Sundstrom, Kathleen B. Cartmell, Ashley A. White, Nicole Russo, Henry Well, Jennifer Young Pierce, Heather M. Brandt, James R. Roberts, Marvella E. Ford
      Abstract: Human papillomavirus (HPV) vaccination prevents 6 HPV-related cancers in men and women. Yet, rates of HPV vaccination among adolescents in the United States lag behind other developed nations, revealing a significant public health issue. This feasibility study tested a collaborative online learning environment to cultivate HPV vaccination champions. A 3-month training program recruited parents to serve as proponents and social media influencers to identify solutions to overcome barriers to HPV vaccination. A mixed methods study design included a pretest survey, three online asynchronous focus groups, a posttest survey, as well as a longitudinal follow-up survey at 6 months. Participants included 22 parents who self-identified as female (95.4%) and white (90.9%). Overall, there was a statistically significant difference in knowledge of HPV and HPV vaccination between pretest and posttest (p = 0.0042). This technology-mediated intervention increased parents' confidence and motivated them to speak more freely about HPV vaccination in-person and online with others in their social networks. Participants identified prevalent misinformation about HPV vaccination and learned how to effectively craft messages to address concerns related to safety and side effects, gender, understanding of risk, and sexual activity. Objective measures and qualitative open-ended assessment showed high intervention engagement and treatment satisfaction. All participants (100%) indicated that they enjoyed participating in the intervention. The effectiveness of this feasibility study suggests that social media is an appropriate platform to empower parents to counter vaccine hesitancy and misinformation through HPV vaccination information that is simple and shareable in-person and online.
      PubDate: 2021-02-15T00:00:00Z
       
  • Marketplace and Literature Review of Spanish Language Mental Health Apps

    • Authors: Alma Oñate Muñoz, Erica Camacho, John Torous
      Abstract: Language differences between patients and providers remains a barrier to accessing health care, especially mental health services. One potential solution to reduce inequities for patients that speak different languages and improve their access to care is through the delivery of healthcare through mobile technology. Given that the Latinx community serves as the largest ethnic minority in the United States, this two-phased review examines Spanish app development, feasibility and efficacy. Phase 1 explored the commercial marketplace for apps available in Spanish, while phase 2 involved a literature review of published research centered around the creation, functions, and usability of these apps using the PubMed and Google Scholar electronic databases. Of the apps available on the database, only 14.5% of them had Spanish operability. The literature search uncovered 629 results, of which 12 research articles that tested or described 10 apps met the inclusion criteria. Of the 10 apps studied in this literature review, only four apps were translated to Spanish. Our study reveals that despite increasing interest in Spanish-language apps to address mental health, the commercial marketplace is not currently meeting the demand.
      PubDate: 2021-02-15T00:00:00Z
       
  • Patent Landscape of Automated Systems for Personalized Health Management
           (ASHM): Features, Shortcomings, and Implications for Developing an Optimal
           ASHM

    • Authors: Andrey Martyushev-Poklad, Dmitry Yankevich
      Abstract: The current struggle of national health care systems against global epidemic of non-communicable diseases (NCD) is both clinically ineffective and cost ineffective. On the other hand, rapid development of systems biology, P4 medicine and new digital and communication technologies are good prerequisites for creating an affordable and scalable automated system for personalized health management (ASHM). The current practice of ASHM is better represented in patent literature (36 relevant documents found in Google Patents and USPTO) than in scientific papers (17 documents found in PubMed and Google Scholar). However, only a small fraction of publications disclose a complete self-sufficient system. Problems that authors of ASHM aim to address, methodological approaches, and the most important technical solutions are reviewed and discussed along with shortcomings and limitations. Technical solutions for ASHM currently commercialized or described in literature generally fail to enable practicable, scalable and affordable automated and individualized screening, monitoring, prevention and correction of human health conditions. They also fail to provide a decision support system to patients that would help effectively prevent major NCD and their complications, be accessible and cost effective, consider individual lifestyle factors and involve patients in management of their individual health. Based on analysis of the literature, models of health and care, we propose conceptual framework for developing an ASHM that would be free from the mentioned problems.
      PubDate: 2021-02-11T00:00:00Z
       
  • Early Warning Signs of a Mental Health Tsunami: A Coordinated Response to
           Gather Initial Data Insights From Multiple Digital Services Providers

    • Authors: Becky Inkster, Digital Mental Health Data Insights Group (DMHDIG
      Abstract: Introduction: The immediate impact of coronavirus 2019 (COVID-19) on morbidity and mortality has raised the need for accurate and real-time data monitoring and communication. The aim of this study is to document the initial observations from multiple digital services providers during the COVID-19 crisis, especially those related to mental health and well-being.Methods: We used email and social media to announce an urgent call for support. Digital mental health services providers (N = 46), financial services providers (N = 4), and other relevant digital data source providers (N = 3) responded with quantitative and/or qualitative data insights. People with lived experience of distress, as service users/consumers, and carers are included as co-authors.Results: This study provides proof-of-concept of the viability for researchers and private companies to work collaboratively toward a common good. Digital services providers reported a diverse range of mental health concerns. A recurring observation is that demand for digital mental health support has risen, and that the nature of this demand has also changed since COVID-19, with an apparent increased presentation of anxiety and loneliness.Conclusion: Following this study, we will continue to work with providers in more in-depth ways to capture follow-up insights at regular time points. We will also onboard new providers to address data representativeness. Looking ahead, we anticipate the need for a rigorous process to interpret insights from an even wider variety of sources in order to monitor and respond to mental health needs.
      PubDate: 2021-02-10T00:00:00Z
       
  • The Potential of Research Drawing on Clinical Free Text to Bring Benefits
           to Patients in the United Kingdom: A Systematic Review of the Literature

    • Authors: Elizabeth Ford, Keegan Curlewis, Emma Squires, Lucy J. Griffiths, Robert Stewart, Kerina H. Jones
      Abstract: Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions.Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits.Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models.Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.
      PubDate: 2021-02-10T00:00:00Z
       
  • Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe:
           Quantifying the Value of Multimodal Clinical-Semiology and Imaging
           Concordance

    • Authors: Ali Alim-Marvasti, Fernando Pérez-García, Karan Dahele, Gloria Romagnoli, Beate Diehl, Rachel Sparks, Sebastien Ourselin, Matthew J. Clarkson, John S. Duncan
      Abstract: Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers.Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria.Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p < 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27).Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability.Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z).
      PubDate: 2021-02-10T00:00:00Z
       
  • Disclosing Pharmacogenetic Feedback of Caffeine via eHealth Channels,
           Assessment of the Methods and Effects to Behavior Change: A Pilot Study

    • Authors: Kerti Alev, Andres Kütt, Margus Viigimaa
      Abstract: Background: The integration of genetic testing into eHealth applications holds great promise for the personalization of disease prevention guidelines. However, relatively little is known about the impact of eHealth applications on an individual's behavior.Aim: The aim of the pilot study was to investigate the effect of the personalized eHealth application approach to behavior change in a 1-month follow-up period on groups with previously known and unknown caffeine impacts.Method: We created a direct-to-consumer approach that includes providing relevant information and personalized reminders and goals on the digital device regarding the caffeine intake for two groups of individuals: the intervention group (IG) with the genetic raw data available and the control group (CG) to test the impact of the same content (article about caffeine metabolism) on participants without the genetic test. Study participants were all Estonians (n = 160).Results: The study suggests that eHealth applications work for short-term behavior change. Participants in the genetic IG tended to increase caffeine intake if they were informed about caffeine not being harmful. They reported feeling better physically and/or mentally after their behavioral change decision during the period of the study.Conclusions: Our pilot study revealed that eHealth applications may have a positive effect for short-term behavior change, regardless of a prior genetic test. Further studies among larger study groups are required to achieve a better understanding about behavior change of individuals in the field of personalized medicine and eHealth interventions.
      PubDate: 2021-02-09T00:00:00Z
       
  • The Use of Immersive Environments for the Early Detection and Treatment of
           Neuropsychiatric Disorders

    • Authors: Robert F. K. Martin, Patrick Leppink-Shands, Matthew Tlachac, Megan DuBois, Christine Conelea, Suma Jacob, Vassilios Morellas, Theodore Morris, Nikolaos Papanikolopoulos
      Abstract: Neuropsychiatric disorders are highly prevalent conditions with significant individual, societal, and economic impacts. A major challenge in the diagnosis and treatment of these conditions is the lack of sensitive, reliable, objective, quantitative tools to inform diagnosis, and measure symptom severity. Currently available assays rely on self-reports and clinician observations, leading to subjective analysis. As a step toward creating quantitative assays of neuropsychiatric symptoms, we propose an immersive environment to track behaviors relevant to neuropsychiatric symptomatology and to systematically study the effect of environmental contexts on certain behaviors. Moreover, the overarching theme leads to connected tele-psychiatry which can provide effective assessment.
      PubDate: 2021-02-04T00:00:00Z
       
  • Digital Predictors of Morbidity, Hospitalization, and Mortality Among
           Older Adults: A Systematic Review and Meta-Analysis

    • Authors: Sofia Daniolou, Andreas Rapp, Celina Haase, Alfred Ruppert, Marlene Wittwer, Alessandro Scoccia Pappagallo, Nikolaos Pandis, Reto W. Kressig, Marcello Ienca
      Abstract: The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with multimorbidity and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.
      PubDate: 2021-02-04T00:00:00Z
       
 
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