AIMC Topic: Health Status

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Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.

Scientific reports Aug 6, 2019
Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activi...
Exercise Humans Deep Learning Female Reproducibility of Results Wearable Electronic Devices Aged Mobile Applications Young Adult Nutrition Surveys Datasets as Topic Adult Health Status Proportional Hazards Models Male Monitoring, Physiologic Locomotion Aging Models, Biological Kaplan-Meier Estimate Middle Aged Telemedicine Accelerometry Anthropometry
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A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.

IEEE journal of biomedical and health informatics Jun 11, 2019
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-rep...
Self Report Humans Telemedicine Machine Learning Heart Diseases Algorithms Monitoring, Ambulatory Cohort Studies Fitness Trackers Health Status
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How do "robopets" impact the health and well-being of residents in care homes? A systematic review of qualitative and quantitative evidence.

International journal of older people nursing May 9, 2019
BACKGROUND: Robopets are small animal-like robots which have the appearance and behavioural characteristics of pets.
Aged Aged, 80 and over Mental Health Animals Quality of Life Humans Health Status Robotics Homes for the Aged Pets
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Construction of medical equipment-based doctor health monitoring system.

Journal of medical systems Apr 10, 2019
The health status of doctors has been overlooked by the society and even the doctors themselves, especially those doctors who work long hours. Their attention is always on patients, so they are more likely to ignore their own health problems. Therefo...
Stress, Psychological Wearable Electronic Devices Health Status Monitoring, Ambulatory Fatigue Cloud Computing Humans Telemetry Neural Networks, Computer Physicians
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Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation Apr 9, 2019
OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions.
Risk Assessment Adolescent Time Factors Treatment Outcome Health Status Predictive Value of Tests Young Adult Liver Transplantation Humans Adult Iran Machine Learning Cross-Sectional Studies Data Mining Decision Support Techniques Retrospective Studies Male Middle Aged Female Risk Factors Aged
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Using Health Chatbots for Behavior Change: A Mapping Study.

Journal of medical systems Apr 4, 2019
This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest...
Health Status Internet Telemedicine Health Behavior Metabolic Diseases Machine Learning Mental Health Communication User-Computer Interface Nervous System Diseases Text Messaging Behavior, Addictive Nutrition Disorders Humans
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A Fuzzy Comprehensive CS-SVR Model-based health status evaluation of radar.

PloS one Mar 18, 2019
The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fau...
Health Status Fuzzy Logic Radar Humans Algorithms Computer Simulation
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Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.

International journal of environmental research and public health Jan 29, 2019
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health condit...
Humans Neural Networks, Computer Female Adult Male Middle Aged ROC Curve Area Under Curve Models, Theoretical Early Diagnosis Diabetes Mellitus Mexico Health Status
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Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectr...
Hepatitis Alanine Transaminase Aspartate Aminotransferases Liver Function Tests Case-Control Studies Liver Cirrhosis Humans Risk Factors Cohort Studies Health Status Biomarkers Aged Diabetes Mellitus, Type 2 Decision Trees Electronic Health Records Male Fatty Liver Supervised Machine Learning Female Middle Aged Logistic Models Non-alcoholic Fatty Liver Disease Disease Progression
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Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

PLoS medicine Nov 30, 2018
BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admi...
Cardiopulmonary Resuscitation Heart Arrest Humans Health Status Machine Learning Australia Decision Support Techniques Registries Databases, Factual Middle Aged New Zealand Retrospective Studies Risk Assessment Clinical Decision-Making Time Factors Female Hospital Mortality Risk Factors Treatment Outcome Aged Male
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