{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T14:29:41Z","timestamp":1790260181701,"version":"4.1.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patient complexity in mental health varies substantially, yet treatment approaches often rely on standardized protocols. Identifying distinct complexity profiles may support stratified care and more personalized intervention planning in depression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify distinct patient complexity profiles in depression across sociodemographic, clinical, and psychosocial indicators and to evaluate their clinical relevance for personalized treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed complete-case data from 270 adults with major depression using a Knowledge Discovery in Databases framework. Twelve indicators were analyzed via Principal Component Analysis followed by K-means clustering. Robustness was evaluated using supervised validation with a Random Forest classifier and SHAP-based interpretability analysis. Between-profile comparisons were conducted, and expert clinicians evaluated clinical relevance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Model selection supported a three-cluster solution (k = 3: Low-, Moderate-, and High-complexity profiles). The solution was validated using a Random Forest classifier with strong performance (accuracy = 0.91). Statistical comparisons showed that the Low-complexity profile (n = 100, 37.0%) was older and more often partnered and employed, with lower depressive symptoms and better personality functioning. The Moderate-complexity profile (n = 87, 32.2%) was younger, predominantly unpartnered, and had the lowest employment rate and medical comorbidity. The High-complexity profile (n = 83, 30.7%) showed the most severe presentation, characterized by higher depressive symptoms, greater childhood maltreatment, and impaired personality functioning. Clinical experts confirmed interpretability and suggested tailored strategies for each profile.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning identified clinically meaningful patient complexity profiles with significant differences across multiple domains. These profiles provide a framework for stratified care and personalized intervention planning, moving beyond one-size-fits-all approaches.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1741860","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T12:59:47Z","timestamp":1770728387000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Patient complexity profiles in depression: a machine learning approach to personalized mental health"],"prefix":"10.3389","volume":"17","author":[{"given":"Paula","family":"Dagnino","sequence":"first","affiliation":[{"name":"Facultad de Psicolog\u00eda y Humanidades, Universidad San Sebasti\u00e1n","place":["Santiago, Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matias","family":"Salinas","sequence":"additional","affiliation":[{"name":"Health Sciences and Engineering, Universidad de 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