Predicting in-hospital mortality for intensive care unit (ICU) patients is key to final clinical outcomes. AI has shown advantaged accuracy but suffers from the lack of explainability. To address this issue, this paper proposes an eXplainable Multimodal Mortality Predictor (X-MMP) approaching an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Furthermore, we introduce an explainable method, namely Layer-Wise Propagation to Transformer, as a proper extension of the LRP method to Transformers, producing explanations over multimodal inputs and revealing the salient features attributed to prediction. Moreover, the contribution of each modality to clinical outcomes can be visualized, assisting clinicians in understanding the reasoning behind decision-making. We construct a multimodal dataset based on MIMIC-III and MIMIC-III Waveform Database Matched Subset. Comprehensive experiments on benchmark datasets demonstrate that our proposed framework can achieve reasonable interpretation with competitive prediction accuracy. In particular, our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.

通过多模态ICU数据，本文提出了一种可解释的多模态死亡预测器（X-MMP）作为一种高效的、可解释的AI解决方案，用于预测住院死亡率。我们的框架采用多模态学习，可接收来自临床数据的异构输入并做出决策，同时引入一种可解释的方法，即层级传递至Transformer，作为对Transformer的LRP方法的适当扩展，在多模态输入上产生解释并揭示与预测相关的显著特征。此外，可以可视化每种模式对临床结果的贡献，帮助临床医生理解决策背后的推理过程。基于MIMIC-III和MIMIC-III波形数据库匹配子集构建了多模态数据集。在基准数据集上进行的综合实验表明，我们提出的框架能够实现合理的解释，并具有竞争力的预测准确性。特别地，我们的框架可以轻松转移到其他临床任务中，促进了对医疗研究中关键因素的发现。

基于多模态ICU数据的住院死亡风险预测的可解释人工智能