MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

TL;DR

MIPO is a novel framework that enhances healthcare representation learning by integrating patient journeys with medical ontologies using a Transformer-based architecture, improving prediction accuracy and interpretability especially in limited data scenarios.

Contribution

It introduces a mutually reinforcing integration of patient journey data and medical ontologies via a graph-embedding module within a Transformer framework, addressing data scarcity and diversity issues.

Findings

Outperforms baseline methods on real-world datasets

Improves diagnosis prediction accuracy in limited data settings

Enhances interpretability of medical embeddings

Abstract

Representation learning on electronic health records (EHRs) plays a vital role in downstream medical prediction tasks. Although natural language processing techniques, such as recurrent neural networks, and self-attention, have been adapted for learning medical representations from hierarchical, time-stamped EHR data, they often struggle when either general or task-specific data are limited. Recent efforts have attempted to mitigate this challenge by incorporating medical ontologies (i.e., knowledge graphs) into self-supervised tasks like diagnosis prediction. However, two main issues remain: (1) small and uniform ontologies that lack diversity for robust learning, and (2) insufficient attention to the critical contexts or dependencies underlying patient journeys, which could further enhance ontology-based learning. To address these gaps, we propose MIPO (Mutual Integration of Patient Journey and Medical Ontology), a robust end-to-end framework that employs a Transformer-based architecture for representation learning. MIPO emphasizes task-specific representation learning through a sequential diagnosis prediction task, while also incorporating an ontology-based disease-typing task. A graph-embedding module is introduced to integrate information from patient visit records, thus alleviating data insufficiency. This setup creates a mutually reinforcing loop, where both patient-journey embedding and ontology embedding benefit from each other. We validate MIPO on two real-world benchmark datasets, showing that it consistently outperforms baseline methods under both sufficient and limited data conditions. Furthermore, the resulting diagnosis embeddings offer improved interpretability, underscoring the promise of MIPO for real-world healthcare applications.