Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

ABSTRACT: Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were i

SUBMITTER: Jawadi Z 

PROVIDER: S-EPMC11424320 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature