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

Artificial Intelligence (2129) Machine Learning (2076) Postoperative Monitoring and Telemonitoring (190) Big Data Analytics for Elder Care (37)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42259, first published .
Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study

Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study

Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study

Authors of this article:

Jung-Yeon Choi1 Author Orcid Image ;   Sooyoung Yoo2 Author Orcid Image ;   Wongeun Song2, 3 Author Orcid Image ;   Seok Kim2 Author Orcid Image ;   Hyunyoung Baek2 Author Orcid Image ;   Jun Suh Lee4 Author Orcid Image ;   Yoo-Seok Yoon5, 6 Author Orcid Image ;   Seonghae Yoon7 Author Orcid Image ;   Hae-Young Lee8, 9 Author Orcid Image ;   Kwang-il Kim1, 9 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

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  2. Liu C, Huang H, Chen M, Zhu M, Yu J. Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia. Aging Clinical and Experimental Research 2025;37(1) View
  3. Wang Y, Yang Y, Li W, Wang Y, Zhang J, Wan J, Meng X, Ji F. Development and Validation of a Risk Predictive Model for Adverse Postoperative Health Status of Elderly Patients Undergoing Major Abdominal Surgery Using Lasso-Logistic Regression. Clinical Interventions in Aging 2025;Volume 20:183 View
  4. Wang G, Xie Y, Bai X, Zhang Y, Guo J. Development and validation comparison of multiple models for perioperative neurocognitive disorders during hip arthroplasty. Scientific Reports 2025;15(1) View
  5. Friedman J, Parchure P, Cheng F, Fu W, Cheertirala S, Timsina P, Raut G, Reina K, Joseph-Jimerson J, Mazumdar M, Freeman R, Reich D, Kia A. Machine Learning Multimodal Model for Delirium Risk Stratification. JAMA Network Open 2025;8(5):e258874 View
  6. Schöler L, Graf L, Airola A, Ritzi A, Simon M, Peltonen L. Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review. JAMIA Open 2025;8(3) View
  7. Limon D, Satish V, Raghavan N, Morris M, Muir M, Rajesh A. Artificial Intelligence in Surgery Revisited: A 2025 Update on Machine Learning for Predicting Complications and Outcomes. The American Surgeon™ 2025 View
  8. Das O, Tang L, Oh E, Suarez J, Theodore N, Azad T. Machine learning models for predicting postoperative delirium in non-cardiac surgery patients — systematic review and meta-analysis. GeroScience 2025 View