The journal of trauma and acute care surgery
Jan 1, 2022
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive model...
The journal of trauma and acute care surgery
Jan 1, 2022
BACKGROUND: Deviation from guidelines is frequent in emergency situations, and this may lead to increased mortality. Probably because of time constraints, 55% is the greatest reported guidelines compliance rate in severe trauma patients. This study a...
Journal of the American Medical Informatics Association : JAMIA
Dec 28, 2021
OBJECTIVE: Clinical registries-structured databases of demographic, diagnosis, and treatment information-play vital roles in retrospective studies, operational planning, and assessment of patient eligibility for research, including clinical trials. R...
Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Dani...
Journal of the American Medical Informatics Association : JAMIA
Jul 30, 2021
OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patien...
IMPORTANCE: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.
OBJECTIVE: The aim of this study was to develop, train, and test different neural network (NN) algorithm-based models to improve the Global Registry of Acute Coronary Events (GRACE) score performance to predict in-hospital mortality after an acute co...
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