A scoping review of the clinical application of machine learning in data-driven population segmentation analysis.

ABSTRACT:

Objective

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.

Materials and methods

MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structure

SUBMITTER: Liu P 

PROVIDER: S-EPMC10436153 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature