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Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016.
Aurélie Névéol1, K Bretonnel Cohen1,2, Cyril Grouin1
1LIMSI, CNRS, Université Paris-Saclay, Orsay, France.
CEUR Workshop Proceedings
|January 9, 2018
Summary
This study evaluated French medical text analysis systems for entity recognition and death cause classification. Systems showed promising performance, particularly in recognizing medical entities and coding causes of death from certificates.
Area of Science:
- Natural Language Processing
- Biomedical Informatics
- Medical Information Extraction
Background:
- The CLEF eHealth evaluation lab advances information extraction from biomedical texts.
- Task 2 in 2016 focused on French narratives and death certificates, building on prior work.
Purpose of the Study:
- To evaluate systems for named entity recognition and normalization in French medical texts.
- To assess systems for classifying causes of death from French death certificates using ICD-10 codes.
Main Methods:
- Named entity recognition and normalization using the Unified Medical Language System (UMLS).
- Classification of causes of death based on the International Classification of Diseases, tenth revision (ICD-10).
- Evaluation using Precision, Recall, and F-measure on MEDLINE, EMEA, and death certificate corpora.
Main Results:
- Highest F-measure for entity recognition was 0.702 (EMEA corpus).
- Highest F-measure for normalized entity recognition was 0.552 (MEDLINE corpus).
- Highest F-measure for death certificate coding was 0.848.
Conclusions:
- Systems demonstrated varying performance across tasks and corpora.
- The study highlights progress in automated medical information extraction and classification for French texts.
Keywords:
Biomedical TextEntity LinkingFrenchNamed Entity RecognitionNatural Language ProcessingText ClassificationUMLSMore Related Videos
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