The use of machine learning based models to predict the severity of community acquired pneumonia in hospitalised patients: A systematic review.

ABSTRACT:

Background

Community acquired pneumonia (CAP) is a common cause of hospital admission. CAP carries significant risk of adverse outcomes including organ dysfunction, intensive care unit (ICU) admission and death. Earlier admission to ICU for those with severe CAP is associated with better outcomes. Traditional prediction models are used in clinical practice to predict the severity of CAP. However, accuracy of predicting severity may be improved by using machine learning (ML) based models with added advantages of automation and speed. This systematic review evaluates the evidence base of ML-prediction tools in predicting CAP severity.

Methods

MEDLINE, EMBASE and PubMed were systematically searched for studies that used ML-based models to predict mortality and/or ICU admission

SUBMITTER: Lythgoe C 

PROVIDER: S-EPMC11791961 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature