A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a large Electronic Patient Record (EPR) data set consisting of diagnoses, medication, and clinical text data for the purpose of adverse drug event (ADE) prediction. The first contribution of this work is an empirical evaluation of two state-of-the-art medical-code based models in terms of objective performance metrics for ADE prediction on diagnosis and medication data. Secondly, as an extension of previous work, we augment an interpretable deep learning architecture to permit numerical risk and clinical text features and demonstrate how this approach yields improved predictive performance compared to the other baselines. Finally, we assess the importance of attention mechanisms in regards to their usefulness for medical code-level and text-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.

为了从医疗记录中进行预测建模和知识提取，已经开发了各种深度学习架构。一些模型非常重视时间注意力机制和衰减因子，作为一种手段，包括关于医疗事件发生的新近度的高度时间相关信息，同时促进医疗代码级别的可解释性。在本研究中，我们将此类模型与包含诊断、药物治疗和临床文本数据的大型电子病历 (EPR) 数据集一起用于药物不良事件 (ADE) 预测。这项工作的第一个贡献是根据诊断和药物数据的 ADE 预测的客观性能指标对两个最先进的基于医学代码的模型进行实证评估。其次，作为先前工作的扩展，我们增强了可解释的深度学习架构，以允许数字风险和临床文本特征，并展示与其他基线相比，这种方法如何提高预测性能。最后，我们评估了注意力机制在医疗代码级和文本级可解释性方面的重要性，这可能有助于对医疗保健领域内 ADE 发生的性质提供新的见解。