Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia.

ABSTRACT: Background: The ability to assess adverse outcomes in patients with community-acquired pneumonia (CAP) could improve clinical decision-making to enhance clinical practice, but the studies remain insufficient, and similarly, few machine learning (ML) models have been developed. Objective: We aimed to explore the effectiveness of predicting adverse outcomes in CAP through ML models. Methods: A total of 2,302 adults with CAP who were prospectively recruited between January 2012 and March 2015 across three cities in South America were extracted from DryadData. After a 70:30 training set: test set split of the data, nine ML algorithms were executed and their diagnostic accuracy was measured mainly by the area under the curve (AUC). The nine ML algorithms included decision t

SUBMITTER: Xu Z 

PROVIDER: S-EPMC9278327 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature