DICE: Deep Significance Clustering for Outcome-Driven Stratification

2020-10-04 health informatics Title + abstract only

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We present deep significance clustering (DICE), a framework for jointly performing representation learning and clustering for "outcome-driven" stratification. Motivated by practical needs in medicine to risk-stratify patients into subgroups, DICE brings self-supervision to unsupervised tasks to generate cluster membership that may be used to categorize unseen patients by risk levels. DICE is driven by a combined objective function and constraint which require a statistically significant associat...

DICE: Deep Significance Clustering for Outcome-Driven Stratification

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