Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding

Chin Wang Cheong*, Kejing Yin, William K. Cheung, Benjamin C.M. Fung, Jonathan Poon

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Representation learning has been applied to Electronic Health Records (EHR) for medical concept embedding and the downstream predictive analytics tasks with promising results. Medical ontologies can also be integrated to guide the learning so the embedding space can better align with existing medical knowledge. Yet, properly carrying out the integration is non-Trivial. Medical concepts that are similar according to a medical ontology may not be necessarily close in the embedding space learned from the EHR data, as medical ontologies organize medical concepts for their own specific objectives. Any integration methodology without considering the underlying inconsistency will result in sub-optimal medical concept embedding and, in turn, degrade the performance of the downstream tasks. In this article, we propose a novel representation learning framework called ADORE (ADaptive Ontological REpresentations) that allows the medical ontologies to adapt their structures for more robust integrating with the EHR data. ADORE first learns multiple embeddings for each category in the ontology via an attention mechanism. At the same time, it supports an adaptive integration of categorical and multi-relational ontologies in the embedding space using a category-Aware graph attention network. We evaluate the performance of ADORE on a number of predictive analytics tasks using two EHR datasets. Our experimental results show that the medical concept embeddings obtained by ADORE can outperform the state-of-The-Art methods for all the tasks. More importantly, it can result in clinically meaningful sub-categorization of the existing ontological categories and yield attention values that can further enhance the model interpretability.

Original languageEnglish
Article number111
JournalACM Transactions on Intelligent Systems and Technology
Issue number6
Early online date26 Sept 2023
Publication statusPublished - 14 Nov 2023

Scopus Subject Areas

  • Theoretical Computer Science
  • Artificial Intelligence

User-Defined Keywords

  • Additional Key Words and PhrasesElectronic health record
  • data mining with ontologies
  • predictive data analytics
  • representation learning


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