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This paper is in the following e-collection/theme issue:

Artificial Intelligence (2458) Clinical Information and Decision Making (2149) New Methods (1201) Machine Learning (2306) Stroke (277) Artificial Intelligence in Surgery and Perioperative Medicine (52)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58021, first published .
Surgeon's gloved hand interacting with futuristic medical displays showing brain scans and heartbeats.

Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

Authors of this article:

Mi-Young Oh1 Author Orcid Image ;   Hee-Soo Kim2 Author Orcid Image ;   Young Mi Jung3 Author Orcid Image ;   Hyung-Chul Lee4, 5 Author Orcid Image ;   Seung-Bo Lee2 Author Orcid Image ;   Seung Mi Lee6, 7, 8, 9 Author Orcid Image
Article Authors Cited by (8) Tweetations Metrics

Mi-Young Oh   1 , MD, PhD ;   Hee-Soo Kim   2 , MS ;   Young Mi Jung   3 , MD ;   Hyung-Chul Lee   4, 5 , MD, PhD ;   Seung-Bo Lee   2 , PhD ;   Seung Mi Lee   6, 7, 8, 9 , MD, PhD

1 Department of Neurology, Sejong General Hospital, Sejong General Hospital, Bucheon-si, Republic of Korea

2 Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Republic of Korea

3 Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

4 Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea

5 Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea

6 Department of Obstetrics and Gynecology, College of Medicine, Seoul National University, Seoul, Republic of Korea

7 Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Republic of Korea

8 Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea

9 Institute of Reproductive Medicine and Population & Medical Big Data Research Center, Seoul National University, Seoul, Republic of Korea

Corresponding Author:

  • Seung Mi Lee, MD, PhD
  • Department of Obstetrics and Gynecology
  • College of Medicine
  • Seoul National University
  • 101 Daehak‐ro, Jongno‐gu
  • Seoul 03080
  • Republic of Korea
  • Phone: 82 2-2072-4857
  • Fax: 82 2-762-3599
  • Email: lbsm@snu.ac.kr