{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:02:16Z","timestamp":1790229736610,"version":"4.1.0"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Return to work (RTW) is a key indicator of recovery for young and middle-aged maintenance hemodialysis (MHD) patients. Timely identification of patients with a low likelihood of RTW is crucial for precision rehabilitation assessment and risk-stratified management. This study aimed to develop and validate a machine learning-based predictive model for identifying influencing factors of current RTW status and enabling risk stratification among young and middle-aged MHD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>From March 2023 to May 2024, 793 young and middle-aged MHD patients were recruited from eight tertiary hospitals in China, with 768 ultimately included (565 in the modeling set and 203 in the external validation set). Demographic characteristics and psychosocial factors were collected via self-administered questionnaires. Five algorithms, including logistic regression, support vector machine, random forest (RF), extreme gradient boosting, and adaptive boosting, were implemented. Hyperparameter tuning was performed using grid search combined with 10-fold nested cross-validation. The optimal algorithm was selected based on comprehensive evaluation metrics, including area under curve (AUC), calibration curves, and net benefit from decision curve analysis. Least absolute shrinkage and selection operator (LASSO) regression was subsequently applied for feature selection. After determining the final feature set, the optimal model was retrained on the modeling set. Internal validation was conducted using nested cross-validation, and external validation was performed on an independent validation set. The final model was explained using SHapley Additive exPlanations and visualized using a web-based calculator.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RF model was identified as the optimal predictive model. LASSO regression further selected 10 feature variables, including RTW self-efficacy, social functioning, chronic comorbidities, social support, social isolation, family functioning, age, dialysis time period, residence and gender. The RF model was retrained using the selected features. The internal validation yielded an AUC of 0.946 (95% CI 0.892\u20130.997), while the external validation achieved an AUC of 0.918 (0.873\u20130.963), indicating good predictive performance and generalizability of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The RF model performs well in predicting current RTW probability among young and middle-aged MHD patients, enabling identification of high-risk non-RTW individuals and key contributing factors. It may serve as a practical screening tool for precision rehabilitation and stratified clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1846282","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:37:44Z","timestamp":1790228264000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a machine learning-based return to work predictive model for young and middle-aged maintenance hemodialysis patients"],"prefix":"10.3389","volume":"14","author":[{"given":"Qingxin","family":"Mai","sequence":"first","affiliation":[{"name":"Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine","place":["Shenzhen, China"]},{"name":"Shenzhen Clinical College of Integrated Chinese and Western Medicine, Guangzhou University of Chinese Medicine","place":["Shenzhen, 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