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We applied dimensionality reduction and unsupervised machine learning algorithms to classify participants into distinct psychological patterns. We then assessed the associations of machine learning\u2010identified psychological patterns and traditional cut\u2010off based psychological symptoms, with various adverse surgery\u2010related outcomes, using logistic and linear regression models while adjusting for other relevant covariates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We successfully established clustering algorithms for 16,298 participants, demonstrating strong consistency in pattern features. Six distinct psychological patterns among participants were identified, including one group with normal psychological functioning and five groups with varying levels of psychological distress. All identified psychological distress patterns were significantly associated with most surgery\u2010related adverse outcomes, both in short\u2010term (e.g., any within\u2010hospital postoperative complication, odds ratios [ORs]\u2009=\u20091.24\u20131.30) and long\u2010term (e.g., cognitive impairment at 12 months postsurgery, 1.29\u20132.35). In contrast, traditional cut\u2010off\u2010based methods identified only 266 patients with significant psychological symptoms, which showed no association with some key short\u2010term outcomes (e.g., length of hospital stay and postoperative complication), though they remained linked to most long\u2010term outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings demonstrate the effectiveness of machine learning in accurately identifying patients with preoperative psychological distress who may require clinical attention, highlighting the potential of these techniques to guide targeted preoperative interventions and ultimately improve surgical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/da\/3990416","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T14:24:19Z","timestamp":1761315859000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning\u2010Based Identification of Preoperative Psychological Distress and Its Association With Adverse Surgery\u2010Related Outcomes: Evidence From the China Surgery and Anesthesia Cohort (CSAC)"],"prefix":"10.1155","volume":"2025","author":[{"given":"Can","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2586-9234","authenticated-orcid":false,"given":"Wenwen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0232-7915","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0593-7551","authenticated-orcid":false,"given":"Yu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4732-6233","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3845-8079","authenticated-orcid":false,"given":"Huan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"e_1_2_16_1_2","doi-asserted-by":"publisher","DOI":"10.1177\/1352458515588973"},{"key":"e_1_2_16_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2648.2009.05101.x"},{"key":"e_1_2_16_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychores.2018.05.012"},{"key":"e_1_2_16_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijso.2020.05.010"},{"key":"e_1_2_16_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jse.2020.04.015"},{"key":"e_1_2_16_6_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291718000624"},{"key":"e_1_2_16_7_2","doi-asserted-by":"publisher","DOI":"10.1177\/0300060515616722"},{"key":"e_1_2_16_8_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-058187"},{"key":"e_1_2_16_9_2","doi-asserted-by":"publisher","DOI":"10.4103\/0366-6999.238154"},{"key":"e_1_2_16_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2019.05.035"},{"key":"e_1_2_16_11_2","doi-asserted-by":"publisher","DOI":"10.1080\/13548500903483486"},{"key":"e_1_2_16_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpcardiol.2022.101474"},{"key":"e_1_2_16_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijoa.2005.05.008"},{"key":"e_1_2_16_14_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0020306"},{"key":"e_1_2_16_15_2","doi-asserted-by":"publisher","DOI":"10.1097\/YCO.0b013e32834e1424"},{"key":"e_1_2_16_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/da.22710"},{"key":"e_1_2_16_17_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-015-4550-z"},{"key":"e_1_2_16_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40140-021-00505-x"},{"key":"e_1_2_16_19_2","first-page":"7","article-title":"The Influence of Preoperative Psychological Distress on Pain and Function After Total Knee Arthroplasty: A Systematic Review and Meta-Analysis","volume":"101","author":"Sorel J. 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