Preoperative frailty for predicting in-hospital mortality in patients after cardiac surgery: an interpretable Machine learning model based on a retrospective multicenter cohort study.

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Title: Preoperative frailty for predicting in-hospital mortality in patients after cardiac surgery: an interpretable Machine learning model based on a retrospective multicenter cohort study.
Authors: Ge YN; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China., Ouyang YM; Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China; School of Anesthesiology, Shandong Second Medical University, Weifang 261053, China., Li QQ; Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China; School of Anesthesiology, Shandong Second Medical University, Weifang 261053, China., Huang J; Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China., Li D; Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China., Wang CL; Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China., Zhu YL; Department of Anesthesiology, Naval Medical Center, Naval Medical University, Shanghai 200052, China. Electronic address: zhuyalin1996@126.com., Wang JF; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China; School of Anesthesiology, Shandong Second Medical University, Weifang 261053, China. Electronic address: jfwang@smmu.edu.cn.
Source: International journal of medical informatics [Int J Med Inform] 2026 Jul 15; Vol. 215, pp. 106437. Date of Electronic Publication: 2026 Apr 16.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1872-8243
DOI:10.1016/j.ijmedinf.2026.106437