Multi-center validation of machine learning model for preoperative prediction of postoperative mortality.

ABSTRACT: Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12-18 clinical variables were used for model training. Logistic regression, random forest classifier, extrem

SUBMITTER: Lee SW 

PROVIDER: S-EPMC9276734 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature