AIMC Topic: Health Status

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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...
Humans Machine Learning Mental Health Health Behavior Metabolic Diseases Nervous System Diseases Text Messaging Behavior, Addictive Nutrition Disorders Communication User-Computer Interface Health Status Internet Telemedicine
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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...
Radar Humans Algorithms Computer Simulation Health Status Fuzzy Logic
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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...
Health Status Area Under Curve Early Diagnosis Middle Aged ROC Curve Models, Theoretical Diabetes Mellitus Mexico Humans Neural Networks, Computer Female Adult Male
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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...
Cohort Studies Aspartate Aminotransferases Aged Health Status Humans Liver Function Tests Male Diabetes Mellitus, Type 2 Decision Trees Biomarkers Middle Aged Fatty Liver Supervised Machine Learning Electronic Health Records Disease Progression Logistic Models Non-alcoholic Fatty Liver Disease Female Case-Control Studies Liver Cirrhosis Hepatitis Risk Factors Alanine Transaminase
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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 Aged Heart Arrest Male Middle Aged Risk Assessment Hospital Mortality Humans Decision Support Techniques Time Factors Treatment Outcome Databases, Factual Machine Learning Health Status Retrospective Studies Australia Clinical Decision-Making Registries Female New Zealand Risk Factors
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Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

PLoS medicine Nov 27, 2018
BACKGROUND: Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an eff...
Automation Young Adult Adult Male Middle Aged Risk Assessment Postoperative Complications Comorbidity Adolescent Health Status Data Mining Surgical Procedures, Operative Electronic Health Records Female Reproducibility of Results Risk Factors Aged Humans Machine Learning Retrospective Studies
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Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

PLoS medicine Nov 20, 2018
BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of ca...
Reproducibility of Results Risk Factors Aged Aged, 80 and over Young Adult Adult Humans Machine Learning Electronic Health Records Female Time Factors Socioeconomic Factors Health Status Age Factors Emergency Service, Hospital Data Mining Male Middle Aged Risk Assessment England Adolescent Sex Factors Health Services Needs and Demand Patient Admission Needs Assessment
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Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network.

Sensors (Basel, Switzerland) Nov 13, 2018
Unmanned aerial vehicles (UAVs) require data-link system to link ground data terminals to the real-time controls of each UAV. Consequently, the ability to predict the health status of a UAV data-link system is vital for safe and efficient operations....
Health Status Robotics Bayes Theorem Remote Sensing Technology Aircraft Humans Solar Energy
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Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Aug 2, 2018
BACKGROUND: The objective of this study was to investigate, in subject with stroke, the exact role as prognostic factor of common inflammatory biomarkers and other markers in predicting motor and/or cognitive improvement after rehabilitation treatmen...
Longitudinal Studies Biomarkers Health Status Female Leukocyte Count Disability Evaluation Male Inpatients Humans Motor Activity Stroke Recovery of Function Time Factors Stroke Rehabilitation Machine Learning Hematocrit Predictive Value of Tests Cognition Decision Support Techniques Inflammation Mediators Treatment Outcome Linear Models Motor Skills
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Fetal health status prediction based on maternal clinical history using machine learning techniques.

Computer methods and programs in biomedicine Jun 14, 2018
BACKGROUND AND OBJECTIVE: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enha...
Pregnancy Female Reproducibility of Results Ultrasonography, Prenatal Diagnosis, Computer-Assisted ROC Curve Perception Congenital Abnormalities Humans Decision Trees Machine Learning Area Under Curve Algorithms Mobile Applications Bayes Theorem Logistic Models Internet Support Vector Machine Telemedicine Health Status Fetus Regression Analysis
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