<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>10(12)</volume><submitter>Bhavnani SK</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications.&lt;h4>Objective&lt;/h4>This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgr</pubmed_abstract><journal>JMIR medical informatics</journal><pagination>e37239</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9773032</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.</pubmed_title><pmcid>PMC9773032</pmcid><pubmed_authors>Zhang W</pubmed_authors><pubmed_authors>Visweswaran S</pubmed_authors><pubmed_authors>Kuo YF</pubmed_authors><pubmed_authors>Bhavnani SK</pubmed_authors><pubmed_authors>Raji M</pubmed_authors></additional><is_claimable>false</is_claimable><name>A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.</name><description>&lt;h4>Background&lt;/h4>A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications.&lt;h4>Objective&lt;/h4>This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgr</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Dec</publication><modification>2025-04-26T09:48:06.231Z</modification><creation>2025-04-06T13:08:00.643Z</creation></dates><accession>S-EPMC9773032</accession><cross_references><pubmed>35537203</pubmed><doi>10.2196/37239</doi></cross_references></HashMap>