Summaries of Knowledge Graph Entities: First Steps to Measure the Relevance of Symptoms to Infer Diseases

Knowledge Graphs (KG) are concerned as one of the most efficient and effective knowledge integration approaches. In health domain, they have proven to be valuable resources that link clinical concepts by meaningful relations. This graph-structured information is usually extensive, and the data density it generates may make it difficult to perform tasks that involve human judgement, where the complexity and amount of information provided must be reduced. Consequently, it is required to develop techniques to reduce that large amount of data to more concise forms that facilitate their usage, visualization and analysis. In this paper, we propose a method for distilling the information available in a knowledge graph by creating entity summaries in the form of bags-of-words (BoW). Specifically, we create summaries of symptoms and diseases to measure their presence in medical records of patients. Our evaluation is focused on a vital healthcare worldwide problem, the early diagnosis of HIV in medical records. The proposed method summarizes the KG entities that represent each sign and symptom of acute HIV infection as a BoW and measures its relevance in a set of medical records. A labelled dataset with clinical notes has been compiled to evaluate the method and the results, with a precision and recall close to 0.6, make us optimistic about its performance as only syntactic matching of terms has been considered.