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.

翻译：预测重症监护病房(ICU)患者的院内死亡率是决定最终临床预后的关键因素。人工智能虽已展现出卓越的预测精度，但其缺乏可解释性。针对这一问题，本文提出了一种可解释多模态死亡率预测器(X-MMP)，该方案基于多模态ICU数据，提供了一种高效的可解释AI解决方案。我们采用多模态学习框架，能够接收来自临床数据的异构输入并进行决策。此外，我们引入了一种名为"层间传播到Transformer"的可解释方法，该方法将LRP方法恰当扩展到Transformer架构，可生成多模态输入的解释信息，并揭示与预测相关的显著特征。临床结果中各模态的贡献度也可实现可视化，辅助临床医生理解决策背后的推理过程。基于MIMIC-III数据集及MIMIC-III波形数据库匹配子集构建了多模态数据集。基准数据集上的综合实验证明，所提框架在保持竞争性预测精度的同时能够提供合理的解释。特别地，该框架可便捷迁移至其他临床任务，有助于促进医疗研究中关键因素的发现。