[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124455-en":3,"doc-seo-124455-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124455,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Toward precision psychological rehabilitation - predicting CBT efficacy in post-stroke depression using machine learning","Retrospective clinical analysis evaluated the potential benefits of cognitive behavioral therapy (CBT) for post-stroke depression (PSD) and developed an interpretable machine learning model to predict individual treatment response. Data from 120 PSD patients receiving CBT and 123 control patients were compared using PHQ-9, GAD-7, and General Self-Efficacy Scale scores. A random forest model outperformed logistic regression and gradient boosting (AUC=0.897), with SHAP and ablation identifying baseline PHQ-9, GAD-7, GSE, and social support as key predictors.","TYPE Original Research PUBLISHED 06 January 2026 DOI 10.3389/fpsyt.2025.1722447  \nOPEN ACCESS  \nEDITED BY  \nChang Cai,  \nUniversity of California, San Francisco, United States  \nREVIEWED BY  \nHengJin Ke,  \nWuhan University, China Fengqin Wang,  \nHubei Normal University, China  \n*CORRESPONDENCE  \nJingyuan Lin  \n [1027336301@qq.com](1027336301@qq.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 10 October 2025  \nREVISED 06 December 2025  \nACCEPTED 11 December 2025  \nPUBLISHED 06 January 2026  \nCORRECTED 28 January 2026  \nCITATION  \nLin J and Yu J (2026) Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning.  \nFront. Psychiatry 16:1722447 .  \ndoi: 10.3389/fpsyt.2025.1722447  \nCOPYRIGHT  \n© 2026 Lin and Yu. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nToward precision psychological rehabilitation: predicting  \nCBT efficacy in post-stroke depression using machine learning  \nJingyuan Lin 1*† and Jiansong Yu 2†  \n1 Department of Rehabilitation Medicine, Fujian Provincial Geriatric Hospital, Fuzhou, China,  \n2 Department of Rehabilitation Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China  \nObjective: This study retrospectively examined the potential benefits of cognitive behavioral therapy (CBT) for post-stroke depression (PSD) and developed an interpretable machine learning model to predict individual treatment response. Methods: Clinical and psychological data from 120 PSD patients receiving CBT and 123 patients in a control group were analyzed. Changes in PHQ-9, GAD-7, and General Self-Efficacy Scale (GSE) scores were compared between groups. Within the CBT cohort, a random forest classifier was trained to predict treatment response and compared with logistic regression and gradient boosting models. SHAP values and ablation analyses were used to assess feature contributions and model interpretability.  \nResults: Baseline characteristics were comparable between groups. The CBT group showed greater improvement in depressive symptoms than the control group. Among predictive models, the random forest classifier demonstrated the highest performance (AUC = 0 . 897; accuracy = 0 . 861) . SHAP and ablation analyses consistently highlighted baseline depressive severity (PHQ-9), anxiety (GAD-7), self-efficacy (GSE), and social support (SSRS) as the most influential predictors of CBT response.  \nConclusion: CBT was associated with greater improvement in depressive symptoms among patients with post-stroke depression; however, causal inferences should be made cautiously given the retrospective design. The proposed machine learning model shows preliminary promise for predicting treatment response, but further validation in prospective and multi-center studies is needed before clinical implementation.  \nKEYWORDS  \ncognitive behavioral therapy (CBT), machine learning, post-stroke depression (PSD), predictive modeling, retrospective analysis  \nFrontiers in Psychiatry 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nPost-stroke depression (PSD) is a common neuropsychiatric complication, affecting approximately 39%–52% of stroke survivorsand contributing to impaired recovery, reduced quality of life, and increased mortality (1) . Although both pharmacological treatmentsand cognitive behavioral therapy (CBT) can be beneficial, accurately predicting treatment response remains a major clinical challenge. Conventional approaches often fail to account for individual variability, and predict","cbCaithUhtrP1fxH","https://ap.wps.com/l/cbCaithUhtrP1fxH","pdf",1484168,1,10,"English","en",105,"# Objective\n# Methods\n## Study design and ethical considerations\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What was the study objective regarding CBT for post-stroke depression?\",\"answer\":\"The study assessed whether CBT benefits patients with post-stroke depression and built an interpretable machine learning model to predict individual treatment response.\"},{\"question\":\"How were treatment responses and baseline predictors evaluated?\",\"answer\":\"Group changes were compared using PHQ-9, GAD-7, and General Self-Efficacy Scale scores, and within the CBT cohort a random forest model was trained and interpreted using SHAP values and ablation analyses.\"},{\"question\":\"Which model performed best and what variables most influenced predictions?\",\"answer\":\"The random forest classifier showed the highest performance (AUC=0.897). SHAP and ablation highlighted baseline depressive severity (PHQ-9), anxiety (GAD-7), self-efficacy (GSE), and social support (SSRS) as most influential predictors.\"}]","Toward precision psychological rehabilitation - predicting CBT efficacy in post-stroke depression using machine learning | PDF",1785822408,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"toward-precision-psychological-rehabilitation-predicting-cbt-efficacy-in-post-stroke-depression-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/toward-precision-psychological-rehabilitation-predicting-cbt-efficacy-in-post-stroke-depression-using-machine-learning/124455/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the study objective regarding CBT for post-stroke depression?","Question",{"text":75,"@type":76},"The study assessed whether CBT benefits patients with post-stroke depression and built an interpretable machine learning model to predict individual treatment response.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were treatment responses and baseline predictors evaluated?",{"text":80,"@type":76},"Group changes were compared using PHQ-9, GAD-7, and General Self-Efficacy Scale scores, and within the CBT cohort a random forest model was trained and interpreted using SHAP values and ablation analyses.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what variables most influenced predictions?",{"text":84,"@type":76},"The random forest classifier showed the highest performance (AUC=0.897). 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