Terminologies augmented recurrent neural network model for clinical named entity recognition

Ivan Lerner; Nicolas Paris; Xavier Tannier

doi:10.1016/j.jbi.2019.103356

Terminologies augmented recurrent neural network model for clinical named entity recognition

J Biomed Inform. 2020 Feb:102:103356. doi: 10.1016/j.jbi.2019.103356. Epub 2019 Dec 16.

Authors

Ivan Lerner¹, Nicolas Paris², Xavier Tannier³

Affiliations

¹ Paris University, Paris, France; AP-HP, DSI-WIND, Paris, France.
² AP-HP, DSI-WIND, Paris, France; LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, F-91405, Orsay, France.
³ Sorbonne Université, Inserm, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006 Paris, France. Electronic address: xavier.tannier@sorbonne-universite.fr.

PMID: 31837473
DOI: 10.1016/j.jbi.2019.103356

Abstract

Objective: 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.

Methods: We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and a hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (Drug names, Signs or symptoms, Diseases or disorders, Diagnostic procedures or lab tests and Therapeutic procedures). 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 h to annotate. The inter-annotator agreement as measured by Cohen's Kappa was substantial for non-exact match (Κ = 0.61) and moderate considering exact match (Κ = 0.42). 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, and i2b2-small a version reduced to match APcNER number of entities.

Results: 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. For i2b2-small and APcNER, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 87.8% versus 85.6% and 86.4% versus 81.9% respectively. 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.

Conclusion: APcNER is a French corpus for clinical-NER of five types of entities which covers a large variety of document types. The extension of the supervised model with terminology has allowed an easy increase in performance, especially for rare entities, and established near state of the art results on the i2b2-2009 corpus.

Keywords: APcNER; Clinical natural language processing; Information extraction; Machine learning; Named entity recognition.

MeSH terms

Language
Natural Language Processing*
Neural Networks, Computer*
Terminology as Topic*