Abstract
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.
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Qin, T., Guan, Y. (2016). A Bootstrapping Approach to Symptom Entity Extraction on Chinese Electronic Medical Records. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_34
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DOI: https://doi.org/10.1007/978-3-319-47674-2_34
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