Xia Eryu, Sun Wen, Mei Jing, Xu Enliang, Wang Ke, Qin Yong
IBM Research, Beijing, China.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1118-1126. eCollection 2018.
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.
疾病-症状关系是一种重要的生物医学关系，可用于临床决策支持，包括构建医学诊断系统。在此，我们展示了一项从海量生物医学文献中挖掘疾病-症状关系并基于该关系构建生物医学知识图谱的研究。我们分析并刻画了来自15970134条MEDLINE/PubMed引用记录中，人类疾病本体中的8514个疾病概念、症状本体中的842个症状概念及其关系的出现情况。我们通过以下方式改进了之前的疾病-症状关系挖掘工作：（1）在医学实体关联发现中利用概念的层次信息；（2）在知识图谱构建中纳入实体之间更精确的带权重关系。基于构建的知识图谱实现了一个用于重症疾病诊断的医学诊断系统，与所有其他方法相比，该系统表现最佳。
