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A promising application of AI to healthcare is the retrieval of informationfrom electronic health records (EHRs), e.g. to aid clinicians in findingrelevant information for a consultation or to recruit suitable patients for astudy. This requires search capabilities far beyond simple string matching,including the retrieval of concepts (diagnoses, symptoms, medications, etc.)related to the one in question. The suitability of AI methods for suchapplications is tested by predicting the relatedness of concepts with knownrelatedness scores. However, all existing biomedical concept relatednessdatasets are notoriously small and consist of hand-picked concept pairs. Weopen-source a novel concept relatedness benchmark overcoming these issues: itis six times larger than existing datasets and concept pairs are chosen basedon co-occurrence in EHRs, ensuring their relevance for the application ofinterest. We present an in-depth analysis of our new dataset and compare it toexisting ones, highlighting that it is not only larger but also complementsexisting datasets in terms of the types of concepts included. Initialexperiments with state-of-the-art embedding methods show that our dataset is achallenging new benchmark for testing concept relatedness models.

Biomedical Concept Relatedness -- A large EHR-based benchmark