中文电子病历的分词及实体识别研究

Purpose/significance] Healthcare big data is an important basic strategic resource in China. Word segmentation and entity recognition of Chinese electronic medical record(EMR) is helpful in extracting important information from a large number of unstructured text.[Method/process] In this study, a Chinese medical thesaurus is firstly built in terms of authoritative medical subject headings, official standards and health website data; then, the effect of four segmentation methods is compared based on the corpus of artificial segmentation and manual annotation; finally, CRF model is used to identify 5 entities, including disease, symptom, test, drug and treatment.[Result/conclusion] Results show that (i)AC automaton model has the best F-measure in EMR word segmentation, which is 82%; (ii) compared with Western medical record, it's difficult to identify medical entities in the record of traditional Chinese medicine. Besides, "Test" and "Disease" entities have better F-measure, while the F-measure of "Symptom" entity is not that ideal.