Outcome prediction aims to predict the future health condition of patients from Electronic Health Record (EHR) data. Because of the sequential characteristic of EHR data, recurrent neural network (RNN)-based outcome prediction methods have achieved state-of-the-art results. However, the major drawback of RNN-based outcome prediction methods is lack of interpretability, which would lead to trust issues. Aiming at this problem, this paper proposes interpretable outcome prediction model with hierarchical attention (IoHAN), an interpretable outcome prediction model by leveraging attention mechanism. The main novelty of IoHAN is that it can pinpoint the fine-grained influence on the final prediction result of each medical component by decomposing the attention weights hierarchically into hospital visits, medical variables, and interactions between medical variables. We evaluated IoHAN on MIMIC-III, a large real-world EHR data set. The experiment results demonstrate that IoHAN can achieve higher prediction accuracy than state-of-the-art outcome prediction models. In addition, the hierarchical decomposed attention weights can interpret the prediction results in a more natural and understandable way.

中文翻译：

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基于电子健康记录和分层注意的可解释结果预测模型

结果预测旨在从电子健康记录 (EHR) 数据中预测患者未来的健康状况。由于 EHR 数据的顺序特征，基于循环神经网络 (RNN) 的结果预测方法取得了最先进的结果。然而，基于 RNN 的结果预测方法的主要缺点是缺乏可解释性，这会导致信任问题。针对这一问题，本文提出了具有层次注意力的可解释结果预测模型（IoHAN），这是一种利用注意力机制的可解释结果预测模型。IoHAN 的主要新颖之处在于它可以通过将注意力权重分层分解为医院就诊、医疗变量、以及医学变量之间的相互作用。我们在大型真实世界 EHR 数据集 MIMIC-III 上评估了 IoHAN。实验结果表明，与最先进的结果预测模型相比，IoHAN 可以实现更高的预测精度。此外，分层分解的注意力权重可以以更自然和易于理解的方式解释预测结果。