A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations.

ABSTRACT: Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 v

SUBMITTER: Morrill J 

PROVIDER: S-EPMC8397870 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature