Machine learning framework for depression subtype grouping: integrating high-resolution imaging and clinical symptom analysis via correlation and clustering.
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| Title: | Machine learning framework for depression subtype grouping: integrating high-resolution imaging and clinical symptom analysis via correlation and clustering. |
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| Authors: | Verma G; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States., Jacob Y; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States., Morris LS; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States., Xing X; Department of Computer Science, University of Nebraska Omaha, Omaha, NE, United States., Delman BN; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States., Murrough JW; Depression and Anxiety Center for Discovery and Treatment, Department of Psychiatry, Icahn School of Medicine of Mount Sinai, New York, NY, United States.; Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States.; VISN 2 Mental Illness Research, Education, and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, United States., Lin AL; Department of Radiology, Division of Biological Sciences, and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, United States., Balchandani P; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States. |
| Source: | Frontiers in psychiatry [Front Psychiatry] 2026 Mar 24; Vol. 17, pp. 1747824. Date of Electronic Publication: 2026 Mar 24 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101545006 Publication Model: eCollection Cited Medium: Print ISSN: 1664-0640 (Print) Linking ISSN: 16640640 NLM ISO Abbreviation: Front Psychiatry Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1664-0640 |
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| DOI: | 10.3389/fpsyt.2026.1747824 |
