Machine learning-based prediction of coronary care unit readmission: A multihospital validation study.

ABSTRACT:

Objective

Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals.

Methods

Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk.

Results

The gradient boosting model was sele

SUBMITTER: Yau FF 

PROVIDER: S-EPMC11367690 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature