A Bootstrapping Approach to Symptom Entity Extraction on Chinese Electronic Medical Records
Symptom entities are widely distributed in Chinese electronic medical records.Previous approaches on symptom entity extraction usually extract continuous strings as symptom entities and require massive human efforts on corpus annotation.We describe the symptom entity as two-tuples of <subject,lesion> and design a soft pattern matching method to locate them in sentences in the EMR.Our bootstrapping approach which only requires a few annotated symptom tuples and it allows iterative extraction from mass electronic medical record databases without human supervision.Furthermore,the described method annotates symptom entities in EMR by the extracted tuples.Starting with 60 annotated entities,our approach reached an F value of 81.40%in the extraction task of 3,150 entities from 992 sets of electronic medical records.
electronic medical record bootstrapping named entity extraction soft matching
Tianyi Qin Yi Guan
Web Intelligence Lab,Research Center of Language Technology,School of Computer Science and Technology,Harbin Institute of technology,150001 Harbin,China
国内会议
第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)
烟台
英文
1-11
2016-10-14（万方平台首次上网日期，不代表论文的发表时间）