Journal
IEEE ACCESS
Volume 7, Issue -, Pages 70624-70633Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2919121
Keywords
Chinese EMRs; entity recognition; information extraction; BiLSTM-CRF; rule-based method
Categories
Funding
- National Natural Science Foundation of China [61802350, 81701687]
- Ministry of Education of China Project of Humanities and Social Sciences [19YJCZH198]
- Educational Commission of Henan, China [17A520050]
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Narrative reports in medical records contain abundant clinical information that may be converted into structured data for managing patient information and predicting trends in diseases. Though various rule-based and machine-learning methods are available in electronic medical records (EMRs), a few works have explored the hybrid methods in extracting information from the Chinese EMRs. In this paper, we developed a novel hybrid approach which integrates the rules and bidirectional long short-term memory with a conditional random field layer (BiLSTM-CRF) model to extract clinical entities and attributes. A corpus of 1509 electronic notes (discharge summaries and operation notes) was annotated. Annotation from three clinicians was reconciled to form a gold standard dataset. The performance of our method was assessed by calculating the precision, recall, and F-measure for two boundary matching strategies. The experimental results demonstrate the effectiveness of our method in clinical information extraction from the Chinese EMRs.
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