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 code 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：利用电子健康记录预测下次就诊诊断是医疗领域的关键任务，对帮助医疗服务提供者和患者制定主动的未来计划至关重要。然而，许多先前研究未能充分处理电子健康记录数据中固有的异质性和层次性特征，不可避免地导致性能欠佳。为此，我们提出NECHO——一种新颖的以医疗代码为中心的多模态对比电子健康记录学习框架，并引入层次正则化。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，整合包含医疗代码、人口统计学信息和临床笔记的多方面信息，所有这些均以医疗代码表征为核心。我们还利用医学本体中的父级层次信息对模态特定编码器进行正则化，以学习电子健康记录数据的层次结构。在MIMIC-III数据集上进行的一系列实验验证了我们方法的有效性。