Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks.

ABSTRACT: Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative

SUBMITTER: Shickel B 

PROVIDER: S-EPMC9867692 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature