CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.

ABSTRACT:

Background

Mortality prediction is critical in clinical care, particularly in intensive care units (ICUs), where early identification of high-risk patients can inform treatment decisions. While deep learning (DL) models have demonstrated significant potential in this task, most suffer from limited generalizability, which hinders their widespread clinical application. Additionally, the class imbalance in electronic health records (EHRs) complicates model training. This study aims to develop a causally-informed prediction model that incorporates underlying causal relationships to mitigate class imbalance, enabling more stable mortality predictions.

Methods

This study introduces the CRISP model (Causal Relationship Informed Superior Prediction), which leverages native counterfa

SUBMITTER: Wang L 

PROVIDER: S-EPMC12001402 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature