  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%) and acceptable for other entity
types in non-exact match (>70%). For drug name recognition on both i2b2-2009
and APcNER, the biGRU-CRF performed better that the terminology-based system,
with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75%
respectively. Moreover, the hybrid system outperformed the biGRU-CRF, with an
exact-match F-measure of 92.2% versus 91.1% (i2b2-2009) and 88.4% versus 81.9%
(APcNER). On APcNER corpus, the micro-average F-measure of the hybrid system on
the 5 entities was 69.5% in exact match, and 84.1% in non-exact match. APcNER
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 near state of the art results on the i2b2-2009 corpus.
