ArticleWorld journal of gastrointestinal surgery2025
Machine learning-based prediction of postoperative mortality risk after abdominal surgery.
Article in World journal of gastrointestinal surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPreoperative risk assessments are vital for identifying patients at high risk of postoperative mortality. However, traditional scoring systems can be time consuming. We hypothesized that the use of machine learning models would enable rapid and accurate risk assessments to be performed.
aimTo assess the potential of machine learning algorithms to develop predictive models of mortality risk after abdominal surgery.
methodsThis retrospective study included 230 individuals who underwent abdominal surgery at the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine between January 2023 and December 2023. Demographic and surgery-related data were collected and used to develop nomogram, decision-tree, random-forest, gradient-boosting, support vector machine, and naïve Bayesian models to predict 30-day mortality risk after abdominal surgery. Models were assessed using receiver operating characteristic curves and compared using the DeLong test.
resultsOf the 230 included patients, 52 died and 178 survived. Models were developed using the training cohort (
conclusionNomogram, random-forest, gradient-boosting tree, and support vector machine models all demonstrate strong performances for the prediction of postoperative mortality and can be selected based on the clinical circumstances.
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