Machine learning algorithms to predict risk of postoperative pneumonia in elderly with hip fracture.

ABSTRACT:

Background

Hip fracture (HF) is one of the most common fractures in the elderly and is significantly associated with high mortality and unfavorable prognosis. Postoperative pneumonia (POP), the most common postoperative complication of HF, can seriously affect patient prognosis and increase the burden on the healthcare system. The aim of this study was to develop machine learning models for identifying elderly patients at high risk of pneumonia after hip fracture surgery.

Methods

From May 2016 to November 2022, patients admitted to a single central hospital for HF served as the study population. We extracted data that could be collected within 24 h of patient admission. The dataset was divided into training and validation sets according to 70:30. Based on the screened risk f

SUBMITTER: Guo J 

PROVIDER: S-EPMC10403839 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature