{ "_id": "6a1eb808b25058d4daade8b5", "Package": "DICErClust", "Title": "Deep Significance Clustering for Clinical Risk Stratification", "Version": "0.1.2", "Authors@R": "c(person(given=\"Sarah\", family=\"Ayton\", , \"saa7050@med.cornell.edu\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-5247-9912\")),\nperson(given=\"Yiye\", family=\"Zhang\", , \"yiz2014@med.cornell.edu\", role = c(\"aut\"), comment = c(ORCID = \"0000-0003-3494-2699\")))", "Description": "We provide an R implementation of Deep Significance\nClustering (DICE), a self-supervised learning framework\ndesigned to identify clinically meaningful and risk-stratified\npatient subgroups from electronic health record (EHR) data.\nDICE jointly optimizes deep representation learning,\nclustering, and outcome prediction while enforcing statistical\nsignificance between predicted outcomes and cluster membership.\nThis integrated optimization produces subgroups that are both\nclinically coherent and predictive, addressing a gap where\ntraditional unsupervised clustering methods and supervised risk\nprediction models alone may fail to generate actionable\nclinical groupings. See Huang et al. 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