{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:01Z","timestamp":1755219841112,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Patients increasingly seek peer support in online health forums; however, the large-scale and inconsistent engagement patterns of these forums often fail to meet patients\u2019 support needs effectively. Smaller, personalized support groups could address these challenges by tailoring interactions to users\u2019 shared experiences and demographics. This study introduces the Group-specific Dirichlet Multinomial Regression (gDMR) model, a structured framework for automating and personalizing support group formation using user-generated content, demographic, and interaction data. By incorporating group-specific parameters and node embeddings, gDMR captures nuanced demographic and behavioral patterns, extending traditional Dirichlet Multinomial Regression (DMR). Experiments demonstrate gDMR\u2019s ability to form groups that are more semantically coherent and contextually relevant than those produced by baseline models. This scalable model reduces manual effort in group personalization, fostering inclusive engagement and enhancing patient-centered care. Findings highlight gDMR\u2019s potential as a framework for digital healthcare platforms, from online health communities to healthcare systems utilizing electronic health records (EHRs), advancing health informatics through support group formation.<\/jats:p>","DOI":"10.3233\/shti250999","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:43Z","timestamp":1754566663000},"source":"Crossref","is-referenced-by-count":0,"title":["Facilitating Online Healthcare Support Group Formation Using Topic Modeling"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0037-3302","authenticated-orcid":false,"given":"Pronob Kumar","family":"Barman","sequence":"first","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-5504","authenticated-orcid":false,"given":"Tera L.","family":"Reynolds","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0935-4182","authenticated-orcid":false,"given":"James","family":"Foulds","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250999","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:43Z","timestamp":1754566663000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250999"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250999","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}