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 counterfactuals to augment the minority class and constructs patient representations by incorporating causal structures to enhance mortality prediction. Patient data were obtained from the public MIMIC-III and MIMIC-IV databases, as well as an additional dataset from the West China Hospital of Sichuan University (WCHSU). RESULTS: A total of 69,190 ICU cases were included, with 30,844 cases from MIMIC-III, 27,362 cases from MIMIC-IV, and 10,984 cases from WCHSU. The CRISP model demonstrated stable performance in mortality prediction across the 3 datasets, achieving AUROC (0.9042-0.9480) and AUPRC (0.4771-0.7611). CRISP's data augmentation module showed predictive performance comparable to commonly used interpolation-based oversampling techniques. CONCLUSION: CRISP achieves better generalizability across different patient groups, compared to various baseline algorithms, thereby enhancing the practical application of DL in clinical decision support. TRIAL REGISTRATION: Trial registration information for the WCHSU data is available on the Chinese Clinical Trial Registry website ( http://www.chictr.org.cn ), with the registration number ChiCTR1900025160. The recruitment period for the data was from August 5, 2019, to August 31, 2021.

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