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.

本研究解决了深度学习在临床应用中缺乏可解释性和推广性的问题，提出了一种基于因果发现的可解释早期预测模型。该模型不仅能提供明确的预测解释，还在应用于不同患者群体时展现出良好的性能。研究表明，所提方法在六种重症病情预测中准确性优于多种基线算法，促进了深度学习在医疗场景中的实际应用。

因果智能深度学习实现可解释和可推广的重症护理结果预测