Automatic Symptom Extraction from Texts to Enhance Knowledge Discovery on Rare Diseases

摘要

This paper reports ongoing researches on automatic symptom recognition towards diagnosis of rare diseases and knowledge acquisition on this subject. We describe a hybrid approach combining sequential pattern mining and natural language processing techniques in order to automate the discovery of symptoms from textual content. More precisely, our weakly supervised approach uses linguistic knowledge to enhance an incremental pattern mining process, in order to filter and make a relevant use of the discovered patterns.