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Chapitre D'ouvrage Année : 2022

Using Deep Learning to Improve Phenotyping from Clinical Reports

Résumé

With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children’s hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.

Dates et versions

hal-03887001 , version 1 (06-12-2022)

Identifiants

Citer

Marc Vincent, Maxime Douillet, Ivan Lerner, Antoine Neuraz, Anita Burgun, et al.. Using Deep Learning to Improve Phenotyping from Clinical Reports. MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation, IOS Press, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220079⟩. ⟨hal-03887001⟩
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