{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:02:33Z","timestamp":1766048553726,"version":"3.48.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020595","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["NSC108-2314-B-195-015"],"award-info":[{"award-number":["NSC108-2314-B-195-015"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2221-E-A49-167-MY3"],"award-info":[{"award-number":["NSTC 113-2221-E-A49-167-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Intradialytic hypotension and hypertension significantly increase cardiovascular risk in hemodialysis patients, highlighting the need for effective monitoring systems. By predicting systolic blood pressure (SBP) values, monitoring capabilities can be improved beyond traditional event classification. Despite this potential advantage, current predictive models report a consistent error range (11\u201313\u00a0mmHg) and operate as black boxes. This study aims to develop a transparent SBP prediction model and evaluate its generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study analyzed data from 524 patients across metropolitan and suburban hospital branches (2016\u201319), collecting hemodialysis parameters, vital signs and predialytic measurements. The Explainable Boosting Machine (EBM) was used as the primary model to predict SBP changes. Cross-branch validation was implemented to evaluate model generalizability and perform feature selection. The EBM model was then compared with five other common machine learning methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>EBM with 16 dynamic features achieved competitive performance compared with other machine learning methods (mean absolute error: 10.57\u201311.33\u00a0mmHg, Pearson\u2019s r: 0.80\u20130.83) in cross-validation between branches. A waterfall plot visualized the model\u2019s additive scoring process, showing how each feature quantitatively contributes to the final SBP prediction. Error analysis of the models revealed strong positive correlations (Pearson\u2019s r: 0.81\u20130.87) between patients\u2019 blood pressure variability and prediction errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The EBM models demonstrated excellent cross-branch generalizability while providing feature-level transparency that reflects clinical understanding of dialysis physiology, unlike black-box models that require additional post-hoc explanation methods such as SHapley Additive exPlanations (SHAP). These findings provide a foundation for developing patient-specific solutions for individuals with highly variable blood pressure patterns.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ckj\/sfaf349","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T13:01:54Z","timestamp":1763125314000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting blood pressure variability in hemodialysis using an explainable boosting machine model"],"prefix":"10.1093","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0990-6734","authenticated-orcid":false,"given":"Wun-Yi","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Biomedical Informatics, National Yang Ming Chiao Tung University , Taipei ,","place":["Taiwan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng-Jui","family":"Lin","sequence":"additional","affiliation":[{"name":"MacKay Memorial Hospital Division of Nephrology, 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,","place":["Taiwan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Jen","family":"Wu","sequence":"additional","affiliation":[{"name":"MacKay Memorial Hospital Division of Nephrology, Department of Internal Medicine, , Taipei ,","place":["Taiwan"]},{"name":"Mackay Medical College Department of Medicine, , New Taipei ,","place":["Taiwan"]},{"name":"China Medical University Hospital, China Medical University Department of Medical Research, , Taichung ,","place":["Taiwan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun-Pin","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Informatics, National Yang Ming Chiao Tung University , Taipei ,","place":["Taiwan"]},{"name":"PhD Program of Interdisciplinary Medicine, National Yang Ming Chiao Tung University , Taipei 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