ArticleJournal of the American Medical Informatics Association : JAMIA2021
Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.Research square · 2026Article
- Development and validation of clinical prediction models for personalized renal function monitoring in people with heart failure in primary care: the RENAL-HF study protocol.European heart journal. Digital health · 2026Article
- Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes.PLOS digital health · 2026Article
- Identifying and characterizing suicide decedent subtypes using deep embedded clustering.Scientific reports · 2025Article
- A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data.NPJ digital medicine · 2025Article
- Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.Nature communications · 2025Article
- Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.PLOS digital health · 2024Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches.JMIR formative research · 2023Article
- Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression.Proceedings of machine learning research · 2023Article
- Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions.Journal of multimorbidity and comorbidityArticle
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Authors and funding
10 authors.
Funding
Abstract
objectiveDeep 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
methodsEnabled 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.
resultsCompared 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.
conclusionDICE 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.