AIMC Topic: Risk Assessment

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Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit.

The American journal of gastroenterology Oct 1, 2020
INTRODUCTION: Acute gastrointestinal (GI) bleed is a common reason for hospitalization with 2%-10% risk of mortality. In this study, we developed a machine learning (ML) model to calculate the risk of mortality in intensive care unit patients admitte...
Humans Machine Learning Intensive Care Units Risk Assessment ROC Curve Area Under Curve APACHE Gastrointestinal Hemorrhage Female Aged Aged, 80 and over Male Middle Aged
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Identification of Diabetes Risk Factors in Chronic Cardiovascular Patients.

Studies in health technology and informatics Sep 4, 2020
Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8 - 84.7 % for the conditional diagnosis of DPN in primary care. Prediction methods that utilize data...
Humans Neural Networks, Computer Machine Learning Risk Factors Risk Assessment Diabetes Mellitus
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Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI).

Pakistan journal of pharmaceutical sciences Sep 1, 2020
This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intelligence based multilayer perceptron (MLP). 300 cases of diabetes mellitus type-2, of age between 18-80 ...
Young Adult Decision Support Techniques Predictive Value of Tests Risk Assessment Humans Female Adult Prevalence Neural Networks, Computer Pakistan Cross-Sectional Studies Risk Factors Adolescent Restless Legs Syndrome Artificial Intelligence Aged Male Incidence Algorithms Aged, 80 and over Middle Aged Diabetes Mellitus, Type 2
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Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?

Clinical orthopaedics and related research Sep 1, 2020
BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As the demand for TKA rises, reducing the risk of revision TKA is becoming increasingly important. Predictive tools based on machine-learning algorithm...
Adult Male Middle Aged Risk Assessment Postoperative Complications Machine Learning Algorithms Female Risk Factors Aged Arthroplasty, Replacement, Knee Reoperation Preoperative Period Osteoarthritis, Knee Knee Injuries Age Factors Denmark Predictive Value of Tests Registries Body Weight Humans
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New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF.

European heart journal. Cardiovascular pharmacotherapy Sep 1, 2020
AIMS: Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from ...
Prothrombin Time Drug Therapy, Computer-Assisted Male Anticoagulants Female Stroke Risk Assessment Blood Coagulation Reproducibility of Results Administration, Oral Time Factors International Normalized Ratio Risk Factors Atrial Fibrillation Humans Treatment Outcome Drug Monitoring Aged Vitamin K Neural Networks, Computer Predictive Value of Tests Registries Aged, 80 and over Hemorrhage Databases, Factual Prospective Studies
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An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.

Journal of the American Medical Informatics Association : JAMIA Aug 1, 2020
OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient dat...
Humans Neural Networks, Computer Artificial Intelligence Pandemics COVID-19 Algorithms Electronic Health Records SARS-CoV-2 Risk Assessment Deep Learning Organizational Case Studies ROC Curve Natural Language Processing Clinical Laboratory Techniques Coronavirus Infections South Carolina Telemedicine Betacoronavirus COVID-19 Testing Pneumonia, Viral
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Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

Computers, informatics, nursing : CIN Aug 1, 2020
The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are diverse and complex. The early prevention of pressure injury and monitoring of these complex high-risk fac...
Humans Machine Learning Forecasting Risk Assessment Chi-Square Distribution Linear Models Pressure Ulcer
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A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players.

Medicine and science in sports and exercise Aug 1, 2020
PURPOSE: To assess injury risk in elite-level youth football (soccer) players based on anthropometric, motor coordination and physical performance measures with a machine learning model.
Soccer Athletic Injuries Cumulative Trauma Disorders Athletic Performance Physical Fitness Anthropometry Belgium Motor Skills Male Humans Risk Assessment Machine Learning Adolescent Prospective Studies Child
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Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to iden...
Machine Learning Algorithms Prospective Studies Electronic Health Records Female Humans Hospitalization Risk Assessment Aged ROC Curve Aged, 80 and over Models, Theoretical Male Workflow Middle Aged Delirium
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Predicting complications of diabetes mellitus using advanced machine learning algorithms.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development.
Risk Assessment Deep Learning Diabetes Mellitus, Type 2 Humans Decision Trees Diabetes Complications Neural Networks, Computer Algorithms Prognosis
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