Machine learning-based prediction of postoperative mortality risk after abdominal surgery DOI
Jihong Yuan, Yongmei Jin,

Jing-Ye Xiang

и другие.

World Journal of Gastrointestinal Surgery, Год журнала: 2025, Номер 17(4)

Опубликована: Март 29, 2025

BACKGROUND Preoperative risk assessments are vital for identifying patients at high of postoperative mortality. However, traditional scoring systems can be time consuming. We hypothesized that the use machine learning models would enable rapid and accurate to performed. AIM To assess potential algorithms develop predictive mortality after abdominal surgery. METHODS This retrospective study included 230 individuals who underwent surgery Seventh People’s Hospital Shanghai University Traditional Chinese Medicine between January 2023 December 2023. Demographic surgery-related data were collected used nomogram, decision-tree, random-forest, gradient-boosting, support vector machine, naïve Bayesian predict 30-day Models assessed using receiver operating characteristic curves compared DeLong test. RESULTS Of patients, 52 died 178 survived. developed training cohort (n = 161) validation 68). The areas under gradient-boosting tree, 0.908 [95% confidence interval (CI): 0.824-0.992], 0.874 (95%CI: 0.785-0.963), 0.928 0.869-0.987), 0.907 0.837-0.976), 0.983 0.959-1.000), 0.807 0.702-0.911), respectively. CONCLUSION Nomogram, all demonstrate strong performances prediction selected based on clinical circumstances.

Язык: Английский

Machine learning-based prediction of postoperative mortality risk after abdominal surgery DOI
Jihong Yuan, Yongmei Jin,

Jing-Ye Xiang

и другие.

World Journal of Gastrointestinal Surgery, Год журнала: 2025, Номер 17(4)

Опубликована: Март 29, 2025

BACKGROUND Preoperative risk assessments are vital for identifying patients at high of postoperative mortality. However, traditional scoring systems can be time consuming. We hypothesized that the use machine learning models would enable rapid and accurate to performed. AIM To assess potential algorithms develop predictive mortality after abdominal surgery. METHODS This retrospective study included 230 individuals who underwent surgery Seventh People’s Hospital Shanghai University Traditional Chinese Medicine between January 2023 December 2023. Demographic surgery-related data were collected used nomogram, decision-tree, random-forest, gradient-boosting, support vector machine, naïve Bayesian predict 30-day Models assessed using receiver operating characteristic curves compared DeLong test. RESULTS Of patients, 52 died 178 survived. developed training cohort (n = 161) validation 68). The areas under gradient-boosting tree, 0.908 [95% confidence interval (CI): 0.824-0.992], 0.874 (95%CI: 0.785-0.963), 0.928 0.869-0.987), 0.907 0.837-0.976), 0.983 0.959-1.000), 0.807 0.702-0.911), respectively. CONCLUSION Nomogram, all demonstrate strong performances prediction selected based on clinical circumstances.

Язык: Английский

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