ArticleBritish journal of anaesthesia2019
Deep-learning model for predicting 30-day postoperative mortality.
Article in British journal of anaesthesia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 4 of them syntheses that pooled it.
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Who cites it
56 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.British journal of anaesthesia · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Effect of health information technology (HIT)-based discharge transition interventions on patient readmissions and emergency room visits: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Effect of telemedicine support for intraoperative anaesthesia care on postoperative outcomes: the TECTONICS randomised clinical trial.British journal of anaesthesia · 2025Trial
- Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.British journal of anaesthesia · 2024Trial
- An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial.JAMA network open · 2023Trial
- Artificial Intelligence for Perioperative Risk Prediction in Anesthesiology: A Bibliometric and Knowledge-Mapping Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Integration of intraoperative data in interpretable machine learning models to predict postoperative AKI in noncardiac surgery patients.JAMIA open · 2026Article
- Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of gastroenterological surgery · 2026Review
- Machine learning in critical care: Improving prediction of mortality and intensive care unit stay after cardiac surgery.JTCVS open · 2026Article
- Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning.Scientific reports · 2026Article
- Article
- The complex task of modelling artificial intelligence workflows for forecasting postoperative risk.Journal of anesthesia, analgesia and critical care · 2025Article
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Machine Learning Insight: Unveiling Overlooked Risk Factors for Postoperative Complications in Gastric Cancer.Cancers · 2025Article
- Impact of societal change on anesthesia practice in Japan.Journal of anesthesia · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Association between disseminated cancer and postoperative 30-day mortality in adult patients with brain tumor craniotomy.Frontiers in oncology · 2025Article
- Predicting Factors Affecting Survival Rate in Patients Undergoing On-Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review.Health science reports · 2025Review
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Abstract
backgroundPostoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality.
methodsWe constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data.
resultsOf 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871).
conclusionsA deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
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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.