Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality.

ABSTRACT:

Aims

This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.

Methods and results

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted

SUBMITTER: Shin S 

PROVIDER: S-EPMC7835549 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature