Predicting next visit diagnosis using Electronic Health Records (EHR) is an essential task in healthcare, critical for devising proactive future plans for both healthcare providers and patients. Nonetheless, many preceding studies have not sufficiently addressed the heterogeneous and hierarchical characteristics inherent in EHR data, inevitably leading to sub-optimal performance. To this end, we propose NECHO, a novel medical code-centric multimodal contrastive EHR learning framework with hierarchical regularisation. First, we integrate multifaceted information encompassing medical codes, demographics, and clinical notes using a tailored network design and a pair of bimodal contrastive losses, all of which pivot around a medical codes representation. We also regularise modality-specific encoders using a parental level information in medical ontology to learn hierarchical structure of EHR data. A series of experiments on MIMIC-III data demonstrates effectiveness of our approach.

翻译：利用电子病历预测下次就诊诊断是医疗健康领域的一项关键任务，对于为医疗服务提供者和患者制定前瞻性计划至关重要。然而，许多已有研究未能充分处理电子病历数据中固有的异质性和层次性特征，这不可避免地导致性能次优。为此，我们提出NECHO，一种新颖的基于医疗代码中心的多模态对比学习框架，并引入了层次正则化方法。首先，我们通过定制的网络设计和一对双模态对比损失函数，将医疗代码、人口统计学信息和临床笔记等多方面信息进行整合，所有这些都以医疗代码表征为核心。我们还利用医学本体中的父级层信息对模态特定编码器进行正则化，以学习电子病历数据的层次结构。在MIMIC-III数据集上的一系列实验证明了我们方法的有效性。