Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study.
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| Title: | Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study. |
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| Authors: | Oh MY; Department of Neurology, Sejong General Hospital, Bucheon-si, Republic of Korea., Kim HS; Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea., Jung YM; Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea., Lee HC; Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.; Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Lee SB; Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea. koreateam23@gmail.com., Lee SM; Department of Obstetrics and Gynecology, College of Medicine, Seoul National University, Seoul, Republic of Korea. lbsm@snu.ac.kr.; Department of Obstetrics and Gynecology&, Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. lbsm@snu.ac.kr.; Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea. lbsm@snu.ac.kr.; Medical Big Data Research Center &, Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University, Seoul, Republic of Korea. lbsm@snu.ac.kr.; Department of Obstetrics and Gynecology & Healthcare AI Research Institute, KoreaMedical Big Data Research Center, Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University Hospital, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. lbsm@snu.ac.kr. |
| Source: | Scientific reports [Sci Rep] 2025 Nov 19; Vol. 15 (1), pp. 40871. Date of Electronic Publication: 2025 Nov 19. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2045-2322 |
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| DOI: | 10.1038/s41598-025-24826-7 |
