Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

ABSTRACT:

Aims

Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge.

Methods and results

We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning-based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision

SUBMITTER: Kim MN 

PROVIDER: S-EPMC11631275 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature