ArticleNPJ digital medicine2022
Multi-center validation of machine learning model for preoperative prediction of postoperative mortality.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Diagnostic systematic review and meta-analysis of machine learning in predicting biochemical recurrence of prostate cancer.Scientific reports · 2025Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.British journal of anaesthesia · 2024Trial
- Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.EBioMedicine · 2026Article
- Machine Learning-Based Prediction of Early Patient-Controlled Analgesia Discontinuation After Total Knee Arthroplasty: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.Korean journal of anesthesiology · 2026Article
- Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of gastroenterological surgery · 2026Review
- Comparative evaluation of feature selection methods for HRV-based survival modeling in HIV-positive ICU patients: a retrospective study.BMC medical informatics and decision making · 2026Article
- Comment on "Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon".Hepatology international · 2026Article
- Machine learning-based prediction of 30-day unplanned readmission risk in day surgery lung cancer patients after lobectomy or sublobectomy: a real-world study.Frontiers in medicine · 2026Article
- Overcoming the data barrier: transfer learning for 90-day mortality prediction in general surgery - a retrospective multicenter development and comparison study.International journal of surgery (London, England) · 2026Article
- Beyond static risk scores: dynamic world models simulating patient-specific trajectories to inform preoperative risk mitigation strategies.Frontiers in medicine · 2026Article
- Artificial Intelligence-Enabled Electrocardiography for Preoperatively Detecting Atrial Fibrillation and Mortality Risk in Patients with Sinus Rhythm.International journal of medical sciences · 2026Article
- Review
- Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.NPJ digital medicine · 2025Article
- 90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study.International journal of surgery (London, England) · 2025Article
- Article
- Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived from cardiopulmonary exercise testing.PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12-18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.