4.6 Article

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

Journal

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocab203

Keywords

machine learning; predictive clustering; risk stratification

Funding

  1. NLM [K01LM013257-01]
  2. Center for Transportation, Environment, and Community Health (CTECH) New Research Initiatives Fund

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DICE is a self-supervised learning framework that can identify clinically similar and risk-stratified subgroups with superior performance metrics and predictive power. Clinical evaluation shows that DICE-generated subgroups have predictive value for outcome prediction.
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 and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. Results: Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. Discussion: DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. Conclusion: DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

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