  We present deep significance clustering (DICE), a framework for jointly
performing representation learning and clustering for "outcome-aware"
stratification. DICE is intended to generate cluster membership that may be
used to categorize a population by individual risk level for a targeted
outcome. Following the representation learning and clustering steps, we embed
the objective function in DICE with a constraint which requires a statistically
significant association between the outcome and cluster membership of learned
representations. DICE further includes a neural architecture search step to
maximize both the likelihood of representation learning and outcome
classification accuracy with cluster membership as the predictor. To
demonstrate its utility in medicine for patient risk-stratification, the
performance of DICE was evaluated using two datasets with different outcome
ratios extracted from real-world electronic health records. Outcomes are
defined as acute kidney injury (30.4\%) among a cohort of COVID-19 patients,
and discharge disposition (36.8\%) among a cohort of heart failure patients,
respectively. Extensive results demonstrate that DICE has superior performance
as measured by the difference in outcome distribution across clusters,
Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index for
clustering, and Area under the ROC Curve (AUC) for outcome classification
compared to several baseline approaches.
