Development and validation of interpretable machine learning models for postoperative pneumonia prediction.

ABSTRACT:

Background

Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients' prognosis and even their lives. This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.

Objective

Our study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. By evaluating the performance differences among these machine learning models, this study aims to assist clinicians in early prediction and diagnosis of POP, providing optimal interventions and treatments.

Methods

Retrospective data from electronic medical records was collected for 264 patients diagnosed with postoperative pneumonia and 26

SUBMITTER: Xiang B 

PROVIDER: S-EPMC11670315 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature