Next Visit Diagnosis Prediction via Medical Code-Centric Multimodal Contrastive EHR Modelling with Hierarchical Regularisation (2024)

Abstract: 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.

## introduction predicting a patient's future diagnosis has been a longstanding objective in both academic and industrial healthcare sectors. its significance is highlighted for healthcare providers with refining decision-making processes and resource allocation, and also for patients with effective future plans. by leveraging the extensive accumulation of ehr data, data-driven deep learning methodologies have achieved considerable advancements in the healthcare practices, particularly in next admissions diagnosis prediction @xcite @xcite . however, most of previous studies have shown limited consideration into multifaceted and hierarchical properties inherent in ehr data. first, it is heterogeneous, encompassing a range of modalities including demographics (e.g. age), medical images (e.g., computed tomography), text (e.g. clinical notes), time series (e.g. laboratory tests), and medical codes (e.g. icd-9). each modality offers diverse and unique perspectives of a single observation and holds substantial potential to improve representative power if it is integrated seamlessly with other modalities. nevertheless, the majority of previous works have solely focused on medical codes or shown limited exploration into effective multimodal fusion strategies @xcite @xcite . second, ehr data employs international classification of diseases (icd) codes @xcite , an organised hierarchical medical concept ontology. it is used by domain experts to systematically categorise patient diagnoses into relevant medical concepts. for instance, in its ninth version (icd-9), circulatory system diseases (icd-9 code 390-459) are further categorised into 9 subcategories, each denoting specific conditions, such as hypertensive disease (icd-9 code 401-405). each is further divided into 10 subcategories (e.g. icd-9 code 401.0 to 401.9). this shows a highly structured and hierarchical dependency amongst them. despite the critical importance of these attributes, they have been largely overlooked in earlier studies. to address the aforementioned characteristics of ehr data, we present a novel framework for next visit diagnosis prediction via medical code-centric multimodal contrastive ehr modelling with hierarchical regularisation (necho). to the best of our knowledge, this framework is the first work designed in a medical code-centric fashion for diagnosis prediction. it tightly and seamlessly entangles three distinct modalities of medical codes, demographics, and clinical notes through a meticulously designed multimodal fusion network and two bimodal contrastive losses. its goal is to boost representational power by positioning demographics and clinical notes as supplementary modalities. furthermore, we harness an auxiliary loss to regularise each modality-specialised encoder based on the ancestral level of medical codes, thereby successfully injecting more general information from the icd-9 medical ontology. therefore, the main contributions of our work are threefold as follows: ## methodology in this section, we firstly introduce notations and problem formulation on next visit diagnosis prediction. thereafter, we describe an overview and details of our proposed framework, necho. ## conclusion next visit diagnosis prediction is beneficial in aidriven healthcare applications and has shown remarkable progress. however, the multifaceted and hierarchical properties of ehr data are beyond the consideration for the most of existing studies. to address these limitations, we introduce the novel multimodal ehr modelling framework, necho. it effectively aggregates representations from three heterogeneous modalities through meticulously de-signed multimodal fusion network and the pair of two bimodal contrastive losses in a medical codecentric manner. it also uses parental level information of icd-9 codes to regularise each modalityspecialised encoder to learn more general information. experimental results including the ablation studies and case study on mimic-iii data highlight the necho's efficacy and superiority.