Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.

ABSTRACT: Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient's individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC-) and precision-recall (PR-) curves an

SUBMITTER: Graeßner M 

PROVIDER: S-EPMC10153050 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature