{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:59:27Z","timestamp":1776373167283,"version":"3.51.2"},"reference-count":69,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T00:00:00Z","timestamp":1694908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.<\/jats:p>","DOI":"10.3390\/s23187946","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T23:57:46Z","timestamp":1694995066000},"page":"7946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3416-8562","authenticated-orcid":false,"given":"Chien Wei","family":"Oei","sequence":"first","affiliation":[{"name":"Management Information Department, Office of Clinical Epidemiology, Analytics and kNowledge (OCEAN), Tan Tock Seng Hospital, Singapore 308433, Singapore"},{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5701-1080","authenticated-orcid":false,"given":"Eddie Yin Kwee","family":"Ng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew Hok Shan","family":"Ng","sequence":"additional","affiliation":[{"name":"Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore 308232, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2086-6517","authenticated-orcid":false,"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[{"name":"National Heart Centre Singapore, Singapore 169609, Singapore"},{"name":"Duke-NUS Medical School, Singapore 169857, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yam Meng","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Vascular Surgery Service, Tan Tock Seng Hospital, Singapore 308433, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lai Gwen","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Tan Tock Seng Hospital, Singapore 308433, Singapore"},{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Udyavara Rajendra","family":"Acharya","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4305, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"ref_1","unstructured":"(2023, August 04). 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