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Medical diagnosis is the process of making a prediction of the disease apatient is likely to have, given a set of symptoms and observations. Thisrequires extensive expert knowledge, in particular when covering a largevariety of diseases. Such knowledge can be coded in a knowledge graph --encompassing diseases, symptoms, and diagnosis paths. Since both the knowledgeitself and its encoding can be incomplete, refining the knowledge graph withadditional information helps physicians making better predictions. At the sametime, for deployment in a hospital, the diagnosis must be explainable andtransparent. In this paper, we present an approach using diagnosis paths in amedical knowledge graph. We show that those graphs can be refined using latentrepresentations with RDF2vec, while the final diagnosis is still made in anexplainable way. Using both an intrinsic as well as an expert-based evaluation,we show that the embedding-based prediction approach is beneficial for refiningthe graph with additional valid conditions.

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph