biostudies-literaturePeng ZHunan Clinical Medical Technology Innovation and Guidance ProjectInnovation Project of Yiyang Science and Technology BureauScientific Research Project of Hunan Health CommitteeHunan University of Traditional Chinese Medicine Joint Fund27699https://www.ebi.ac.uk/biostudies/studies/S-EPMC12307615biostudies-literatureUnknown15(1)Hemodialysis stands as the most prevalent renal replacement therapy globally. Accurately identifying mortality among hemodialysis patients is paramount importance, as it enables the formulation of tailored interventions and facilitates timely management. The objective of the study was to establish and validate an artificial intelligence (AI) model to predict mortality among hemodialysis patients. The data of 559 patients with hemodialysis at a large tertiary hospital were retrospectively analyzed, and those of 82 patients were extracted from another tertiary hospital. The patients from the large tertiary hospital constituted the model development cohort, and the patients from another tertiary hospital constituted the external validation cohort. The patients in the model development cohort Scientific reportsAn artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.PMC12307615B2023031090982024YR212023XYLH0852021SK51811Liao ZXia YTang ZLei MYu FPeng ZLi XMa CLong WZhao SZhong SHe ZFu HfalseAn artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.Hemodialysis stands as the most prevalent renal replacement therapy globally. Accurately identifying mortality among hemodialysis patients is paramount importance, as it enables the formulation of tailored interventions and facilitates timely management. The objective of the study was to establish and validate an artificial intelligence (AI) model to predict mortality among hemodialysis patients. The data of 559 patients with hemodialysis at a large tertiary hospital were retrospectively analyzed, and those of 82 patients were extracted from another tertiary hospital. The patients from the large tertiary hospital constituted the model development cohort, and the patients from another tertiary hospital constituted the external validation cohort. The patients in the model development cohort 2025-01-01T00:00:00Z2025 Jul2026-03-18T14:03:31.702Z2025-08-23T03:06:57.728ZS-EPMC123076154073080910.1038/s41598-025-06576-8