ArticleBMC medical informatics and decision making2019
Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A framework for normalized extraction of fine-grained traditional Chinese medicine symptom entities and relations.BMC medical informatics and decision making · 2025Article
- Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine.BMC medical informatics and decision making · 2020Article
- Construction, evaluation, and application of an electronic medical record corpus for cerebral palsy rehabilitation.Digital healthArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic Medical Records(EMRs) contain much medical information about patients. Medical named entity extracting from EMRs can provide value information to support doctors' decision making. The research on information extraction of Chinese Electronic Medical Records is still behind that has done in English.
methodsThis paper proposed a practical annotation scheme for medical entity extraction on Resident Admit Notes (RANs), and a model which can automatic extract medical entity. Nine types of clinical entities, four types of clinical relationships were defined in our annotation scheme. An end-to-end deep neural network with convolution neural network and long-short term memory units was applied in our model for the medical named entity recognition(NER).
resultWe annotated RANs in three rounds. The overall F-score of annotation consistency was up to 97.73%. And our NER model on the 255 annotated RANs achieved the best F-score of 91.08%.
conclusionThe annotation scheme and the model for NER in this paper are effective to extract medical named entity from RANs and provide the basis for fully excavating the patient's information.
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