Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

ABSTRACT: OBJECTIVES:The goal of this study was to use machine learning to more accurately predict survival after echocardiography. BACKGROUND:Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. METHODS:Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. The authors investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant

SUBMITTER: Samad MD 

PROVIDER: S-EPMC6286869 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature