Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge.

ABSTRACT:

Background

Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients.

Methods

CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using

SUBMITTER: Tian J 

PROVIDER: S-EPMC10053412 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature