Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
Recent advances in deep learning (DL) have prompted the development of
high-performing early warning score (EWS) systems, predicting clinical
deteriorations such as acute kidney injury, acute myocardial infarction, or
circulatory failure. DL models have proven to be powerful tools for various
tasks but come with the cost of lacking interpretability and limited
generalizability, hindering their clinical applications. To develop a practical
EWS system applicable to various outcomes, we propose causally-informed
explainable early prediction model, which leverages causal discovery to
identify the underlying causal relationships of prediction and thus owns two
unique advantages: demonstrating the explicit interpretation of the prediction
while exhibiting decent performance when applied to unfamiliar environments.
Benefiting from these features, our approach achieves superior accuracy for 6
different critical deteriorations and achieves better generalizability across
different patient groups, compared to various baseline algorithms. Besides, we
provide explicit causal pathways to serve as references for assistant clinical
diagnosis and potential interventions. The proposed approach enhances the
practical application of deep learning in various medical scenarios.
