{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T09:05:08Z","timestamp":1784019908115,"version":"3.55.0"},"reference-count":50,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Intradialytic hypotension (IDH) is a common and serious complication during hemodialysis (HD), associated with increased morbidity and mortality. This study aims to develop an artificial intelligence (AI)-driven early warning system to predict IDH events and recommend real-time dialysis parameter adjustments to enhance patient safety.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A total of 9,634 HD sessions from 178 patients were collected. Advanced feature engineering techniques were applied, including domain-specific variable generation, Synthetic Minority Oversampling Technique (SMOTE), and Recursive Feature Elimination with Cross-Validation (RFECV). An AutoML pipeline tested 100 hyperparameter combinations per run to tailor hospital-specific models. This approach enables rapid development of customized models optimized for institutional variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the evaluated models, CatBoost and LightGBM with Extra Trees achieved the highest performance. CatBoost yielded the best results with a ROC-AUC of 0.934 and an F1-macro score of 0.508. Feature selection and SMOTEEEN resampling further enhanced performance, improving the ROC-AUC to 0.949.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The implementation of AutoML for large-scale hyperparameter optimization enables the efficient development of hospital-specific AI models with minimal manual input. This approach supports scalable customization across diverse clinical settings. Our findings underscore the value of AI-powered prediction systems in facilitating early identification and management of intradialytic hypotension (IDH), thereby contributing to improved patient safety and hemodialysis outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261438936","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T12:06:43Z","timestamp":1783771603000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["AutoML-driven ensemble learning for intradialytic hypotension prediction"],"prefix":"10.1177","volume":"12","author":[{"given":"Chih-Yang","family":"Cheng","sequence":"first","affiliation":[{"name":"National Chung Cheng University"},{"name":"Kaohsiung Veterans General Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5791-9883","authenticated-orcid":false,"given":"Yu-Chun","family":"Lin","sequence":"additional","affiliation":[{"name":"Chung Shan Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3493-754X","authenticated-orcid":false,"given":"Anna Nai-Yun","family":"Tung","sequence":"additional","affiliation":[{"name":"University of Southern California,"},{"name":"International Academia of Biomedical Innovation Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiang-Wei","family":"Hu","sequence":"additional","affiliation":[{"name":"International Academia of Biomedical Innovation Technology"},{"name":"Taiwan Artificial Intelligence Association"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I.-Chiu","family":"Chang","sequence":"additional","affiliation":[{"name":"National Chung Cheng University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"e_1_3_7_2_2","article-title":"Global Prevalence of Chronic Kidney Disease and Its Impact on Public Health","author":"Kovesdy C","year":"2022","unstructured":"Kovesdy C. 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