Implementation of a machine learning application in preoperative risk assessment for hip repair surgery.

ABSTRACT:

Background

This study aims to develop a machine learning-based application in a real-world medical domain to assist anesthesiologists in assessing the risk of complications in patients after a hip surgery.

Methods

Data from adult patients who underwent hip repair surgery at Chi-Mei Medical Center and its 2 branch hospitals from January 1, 2013, to March 31, 2020, were analyzed. Patients with incomplete data were excluded. A total of 22 features were included in the algorithms, including demographics, comorbidities, and major preoperative laboratory data from the database. The primary outcome was a composite of adverse events (in-hospital mortality, acute myocardial infarction, stroke, respiratory, hepatic and renal failure, and sepsis). Secondary outcomes were intensive care

SUBMITTER: Li YY 

PROVIDER: S-EPMC9034633 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature