Terminologies augmented recurrent neural network model for clinical named entity recognition
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We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and an hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (drug name, sign or symptom, disease or disorder, diagnostic procedure or lab test and therapeutic procedure). We evaluated each NER systems using exact and partial match definition of F-measure for NER. The APcNER contains 4,837 entities which took 28 hours to annotate, the inter-annotator agreement was acceptable for Drug name in exact match (85 types in non-exact match (>70 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1 respectively. Moreover, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 92.2 (APcNER). On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5 is a French corpus for clinical-NER of five type of entities which covers a large variety of document types. Extending supervised model with terminology allowed for an easy performance gain, especially in low regimes of entities, and established state of the art results on the i2b2-2009 corpus.
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