Phenotyping to predict 12-month health outcomes of older general medicine patients.

ABSTRACT:

Background

A variety of unsupervised learning algorithms have been used to phenotype older patients, enabling directed care and personalised treatment plans. However, the ability of the clusters to accurately discriminate for the risk of older patients, may vary depending on the methods employed.

Aims

To compare seven clustering algorithms in their ability to develop patient phenotypes that accurately predict health outcomes.

Methods

Data was collected for N = 737 older medical inpatients during their hospital stay for five different types of medical data (ICD-10 codes, ATC drug codes, laboratory, clinic and frailty data). We trialled five unsupervised learning algorithms (K-means, K-modes, hierarchical clustering, latent class analysis (LCA), and DBSCAN) and two grap

SUBMITTER: Woodman RJ 

PROVIDER: S-EPMC11846751 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature