An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

ABSTRACT: BACKGROUND:Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. METHODS:We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between Ap

SUBMITTER: Hill BL 

PROVIDER: S-EPMC6883494 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature