[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.
Wang Ruojia
,
Cho Sang
,
Wang Jimin
. Healthcare Data Mining: Word Segmentation and Named Entity Recognition in Chinese Electronic Medical Record[J]. Library and Information Service, 2019
, 63(2)
: 34
-42
.
DOI: 10.13266/j.issn.0252-3116.2019.02.004
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