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

Machine Learning (1501) Clinical Information and Decision Making (1434) Perioperative Risk Assessment (14) Surgery, Trauma and Surgical Sciences (146) Artificial Intelligence in Surgery and Perioperative Medicine (27) Artificial Intelligence (1421) Myocardial Infarction (Heart Attack) (45)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32771, first published .
Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Authors of this article:

Seo Jeong Shin1 Author Orcid Image ;   Jungchan Park1, 2 Author Orcid Image ;   Seung-Hwa Lee3, 4 Author Orcid Image ;   Kwangmo Yang1, 5 Author Orcid Image ;   Rae Woong Park1, 6 Author Orcid Image
Article Authors Cited by (7) Tweetations (8) Metrics

Journals

  1. Nirvik P, Kertai M. Future of Perioperative Precision Medicine: Integration of Molecular Science, Dynamic Health Care Informatics, and Implementation of Predictive Pathways in Real Time. Anesthesia & Analgesia 2022 View
  2. Oh A, Park J, Shin S, Choi B, Lee J, Lee S, Yang K. Prediction model for myocardial injury after non-cardiac surgery using machine learning. Scientific Reports 2023;13(1) View
  3. Duceppe E. Cardiovascular complications after noncardiac surgery: New insights in surveillance and management. Perioperative Care and Operating Room Management 2022;29:100276 View
  4. Lee D, Oh A, Park J, Lee S, Choi B, Yang K, Kim H, Park R. Machine learning-based prediction model for postoperative delirium in non-cardiac surgery. BMC Psychiatry 2023;23(1) View
  5. Arina P, Kaczorek M, Hofmaenner D, Pisciotta W, Refinetti P, Singer M, Mazomenos E, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024;140(1):85 View
  6. Zhang K, Liu C, Sha X, Yao S, Li Z, Yu Y, Lou J, Fu Q, Liu Y, Cao J, Zhang J, Yang Y, Mi W, Li H. Development and validation of a prediction model to predict major adverse cardiovascular events in elderly patients undergoing noncardiac surgery: A retrospective cohort study. Atherosclerosis 2023;376:71 View
  7. Salih A, Galazzo I, Gkontra P, Rauseo E, Lee A, Lekadir K, Radeva P, Petersen S, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artificial Intelligence Review 2024;57(9) View