Machine learning approaches for electronic health records phenotyping: a methodical review.

ABSTRACT:

Objective

Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used.

Materials and methods

We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies.

Results

Most studies utilized data from a single institution and included infor

SUBMITTER: Yang S 

PROVIDER: S-EPMC9846699 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature