XAI for In-hospital Mortality Prediction via Multimodal ICU Data

TL;DR

This paper introduces X-MMP, an explainable AI framework that predicts in-hospital ICU mortality using multimodal data, providing interpretable results to assist clinical decision-making.

Contribution

It presents a novel multimodal learning framework with an explainability method for ICU mortality prediction, enhancing interpretability and transferability in clinical AI applications.

Findings

Achieves competitive prediction accuracy on MIMIC datasets.

Provides meaningful explanations over multimodal inputs.

Visualizes modality contributions to outcomes.

Abstract

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.