Complex deep learning models show high prediction tasks in various clinical
prediction tasks but their inherent complexity makes it more challenging to
explain model predictions for clinicians and healthcare providers. Existing
research on explainability of deep learning models in healthcare have two major
limitations: using post-hoc explanations and using raw clinical variables as
units of explanation, both of which are often difficult for human
interpretation. In this work, we designed a self-explaining deep learning
framework using the expert-knowledge driven clinical concepts or intermediate
features as units of explanation. The self-explaining nature of our proposed
model comes from generating both explanations and predictions within the same
architectural framework via joint training. We tested our proposed approach on
a publicly available Electronic Health Records (EHR) dataset for predicting
patient mortality in the ICU. In order to analyze the
performance-interpretability trade-off, we compared our proposed model with a
baseline having the same set-up but without the explanation components.
Experimental results suggest that adding explainability components to a deep
learning framework does not impact prediction performance and the explanations
generated by the model can provide insights to the clinicians to understand the
possible reasons behind patient mortality.

本研究提出了一种基于专家知识的临床概念或中间特征作为解释单元的自我解释深度学习框架，并在公开的电子健康记录数据集上进行了实验。实验结果表明，该模型的解释性组件不会影响预测性能，并且模型生成的解释可以为医生提供了解病人死亡原因的洞见。