Mining Disease-Symptom Relation from Massive Biomedical Literature and Its Application in Severe Disease Diagnosis

Abstract

Disease-symptom relation is an important biomedical relation that can be used for clinical decision support including building medical diagnostic systems. Here we present a study on mining disease-symptom relation from massive biomedical literature and constructing biomedical knowledge graph from the relation. From 15,970,134 MEDLINE/PubMed citation records, occurrences of 8,514 disease concepts from the Human Disease Ontology and 842 symptom concepts from the Symptom Ontology and their relation were analyzed and characterized. We improve previous disease-symptom relation mining work by: (1) leveraging the hierarchy information of concepts in medical entity association discovery; and (2) including more exquisite relationship with weights between entities for knowledge graph construction. A medical diagnostic system for severe disease diagnosis was implemented based on the constructed knowledge graph and achieved the best performance compared to all other methods.