SIBM at CLEF eHealth Evaluation Lab 2017: Multilingual Information Extraction with CIM-IND

This paper presents SIBM’s participation in the Task 1: Multilingual Information Extraction ICD10 coding of the CLEF eHealth 2017 evaluation initiative which focuses on named entity recognition in French and English death certificates. We addressed the identification of relevant clinical entities within the International Classification of Diseases version 10 (ICD10) in the CépiDC and CDC datasets with our CIM-IND system. CIM-IND is a multilingual system designed to recognize named entities in French and English texts using a dictionary-based approach and natural language processing and fuzzy matching methods. The evaluation was performed for two cases: (i) for all ICD10 codes, the main evaluation for the task and (ii) for ICD10 codes addressing a particular type of deaths, called external causes or violent deaths. On the English test set, our system obtained F-scores of 0.81 for all ICD10 codes and 0.4066 for external causes. On the French aligned test set, our system obtained F-scores of 0.8038 for all ICD10 codes and 0.5011 for external causes. On the French raw test set, our system obtained Fscores of 0.7636 for all ICD10 codes and 0.4897 for external causes. These scores were substantially higher than the average score of the systems that participated in the challenge.