Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.

ABSTRACT:

Background

Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.

Methods

This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause read

SUBMITTER: Huang Y 

PROVIDER: S-EPMC9643900 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature