Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.

ABSTRACT:

Objective

Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate.

Materials and methods

Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes

SUBMITTER: Huang Y 

PROVIDER: S-EPMC8500061 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature