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：利用电子健康记录（EHR）预测下一次就诊诊断是医疗领域的一项关键任务，对医疗机构和患者制定前瞻性计划至关重要。然而，现有研究大多未能充分处理EHR数据中固有的异构性和层次性特征，导致性能次优。为此，我们提出NECHO——一种基于医疗编码为中心、结合分层正则化的新型多模态对比EHR学习框架。首先，通过定制化网络设计和一对双模态对比损失函数，整合包含医疗编码、人口统计学信息和临床笔记的多层面信息，所有信息均围绕医疗编码表示进行组织。同时，利用医学本体中的父级层次信息对模态专用编码器进行正则化处理，以学习EHR数据的层次结构。在MIMIC-III数据集上的系列实验验证了本方法的有效性。