Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

ABSTRACT:

Aims

Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing predictive models in real-world situations. This study aimed to develop a machine learning model for predicting 90 day readmission for HFpEF patients.

Methods and results

Data were extracted from electronic health records from 1 August 2020 to 1 August 2021 and follow-up records of patients with HFpEF within 3 months after discharge. Feature extraction was performed by univariate analysis combined with the least absolute shrinkage and selection operator (LASSO) algorithms. Machine learning models like eXtreme Gradient Boosting (XG

SUBMITTER: Zheng B 

PROVIDER: S-EPMC11631356 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature