Electronic health records (EHRs) are a valuable source of information that can aid in understanding a patient’s health condition and making informed healthcare decisions. However, modelling longitudinal EHRs with heterogeneous information is a challenging task. Although recurrent neural networks (RNNs) are frequently utilized in artificial intelligence (AI) models for capturing longitudinal data, their explanatory capabilities are limited. Predictive clustering stands as the most recent advancement within this domain, offering interpretable indications at the cluster level for predicting disease risk. Nonetheless, the challenge of determining the optimal number of clusters has put a brake on the widespread application of predictive clustering for disease risk prediction. In this paper, we introduce a novel non-parametric predictive clustering-based risk prediction model that integrates the Dirichlet Process Mixture Model (DPMM) with predictive clustering via neural networks. To enhance the model’s interpretability, we integrate attention mechanisms that enable the capture of local-level evidence in addition to the cluster-level evidence provided by predictive clustering. The outcome of this research is the development of a multi-level explainable artificial intelligence (AI) model. We evaluated the proposed model on two real-world datasets and demonstrated its effectiveness in capturing longitudinal EHR information for disease risk prediction. Moreover, the model successfully produced interpretable evidence to bolster its predictions.

中文翻译：

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通过可解释的风险预测人工智能模型增强医疗决策支持

电子健康记录 (EHR) 是一种宝贵的信息来源，有助于了解患者的健康状况并做出明智的医疗保健决策。然而，利用异构信息对纵向 EHR 进行建模是一项具有挑战性的任务。尽管循环神经网络（RNN）经常在人工智能（AI）模型中用于捕获纵向数据，但它们的解释能力有限。预测聚类是该领域的最新进展，在聚类级别提供可解释的指示来预测疾病风险。尽管如此，确定最佳聚类数量的挑战阻碍了预测聚类在疾病风险预测中的广泛应用。在本文中，我们介绍了一种新颖的基于非参数预测聚类的风险预测模型，该模型通过神经网络将狄利克雷过程混合模型（DPMM）与预测聚类相结合。为了增强模型的可解释性，我们集成了注意力机制，除了预测聚类提供的集群级证据之外，还能够捕获本地级证据。这项研究的成果是开发了多层次可解释的人工智能（AI）模型。我们在两个真实世界数据集上评估了所提出的模型，并证明了其在捕获纵向 EHR 信息以进行疾病风险预测方面的有效性。此外，该模型成功地产生了可解释的证据来支持其预测。