# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "DICErClust" in publications use:' type: software license: MIT title: 'DICErClust: Deep Significance Clustering for Clinical Risk Stratification' version: 0.1.2 doi: 10.32614/CRAN.package.DICErClust abstract: We provide an R implementation of Deep Significance Clustering (DICE), a self-supervised learning framework designed to identify clinically meaningful and risk-stratified patient subgroups from electronic health record (EHR) data. DICE jointly optimizes deep representation learning, clustering, and outcome prediction while enforcing statistical significance between predicted outcomes and cluster membership. This integrated optimization produces subgroups that are both clinically coherent and predictive, addressing a gap where traditional unsupervised clustering methods and supervised risk prediction models alone may fail to generate actionable clinical groupings. See Huang et al. (2021) . authors: - family-names: Ayton given-names: Sarah email: saa7050@med.cornell.edu orcid: https://orcid.org/0000-0001-5247-9912 - family-names: Zhang given-names: Yiye email: yiz2014@med.cornell.edu orcid: https://orcid.org/0000-0003-3494-2699 repository: https://cran.r-universe.dev commit: 5c66e4d5d622c2a2a9018a5fa2129653e66f5e1e date-released: '2026-05-28' contact: - family-names: Ayton given-names: Sarah email: saa7050@med.cornell.edu orcid: https://orcid.org/0000-0001-5247-9912