A machine learning-based model for assessing community-acquired pneumonia severity using routine blood tests.

ABSTRACT:

Background

The choice of first-line therapy for community-acquired pneumonia (CAP) depends on disease severity. However, quickly and accurately differentiating mild from severe CAP patients remains challenging. This study aims to evaluate the performance of machine learning-based diagnostic models employing routine blood indicators to distinguish CAP severity.

Methods

A multicenter, retrospective, case-control study conducted at Xuhui Central Hospital (Discovery cohort), and Putuo People's Hospital (Validation cohort), from January 2016 to January 2024. Patients were further classified into mild or severe CAP according to the IDSA/ATS criteria. Routine blood tests were performed with an automatic blood cell analyzer. Twelve machine learning-based diagnostic models were devel

SUBMITTER: Guan C 

PROVIDER: S-EPMC12832646 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature