Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.

ABSTRACT:

Objective

To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours.

Background

Prediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models.

Methods

All patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospit

SUBMITTER: Chiew CJ 

PROVIDER: S-EPMC7668340 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature