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.
翻译:复杂的深层次学习模型显示各种临床预测任务中的高预测任务,但其内在复杂性使得解释临床医生和保健提供者的模型预测更具挑战性。关于保健领域深层次学习模型的解释性的现有研究有两大局限性:使用休克后的解释和使用原始临床变量作为解释单位,两者通常都难以对人进行解释。在这项工作中,我们设计了一个自我解释的深层次学习框架,使用专家-知识驱动的临床概念或中间特征作为解释单位。我们提议的模型的自我解释性质来自通过联合培训在同一建筑框架内提出解释和预测。我们测试了我们为预测疾病死亡率而公开提供的电子健康记录(EHR)数据集的拟议方法。为了分析性能可解释性交易,我们将我们提议的模型与具有相同设置但没有解释组成部分的基线进行了比较。实验结果表明,为深层次学习框架增加解释性组成部分不会影响预测性能,模型产生的解释性能可以向临床医生提供深刻的见解,以了解病人死亡率背后的可能原因。