ID: 20231012155317928-1315

Researcher: Jamil Zaghir, Jean-Philippe Goldman, Mina Bjelogrlic, Daniel Keszthelyi, Christophe Gaudet-Blavignac, Hugues Turbé, Belinda Lokaj, Christian Lovis

WP: Other

PI: Christian Lovis

Abstract: Many medical narratives are read by care professionals in their preferred language. These documents can be produced by organizations, authorities or national publishers. However, they are often hardly findable using the usual query engines based on English such as PubMed. This work explores the possibility to automatically categorize medical documents in French following an automatic Natural Language Processing pipeline. The pipeline is used to compare the performance of 6 different machine learning and deep neural network approaches on a large dataset of peer-reviewed weekly published Swiss medical journal in French covering major topics in medicine over the last 15 years. An accuracy of 96% was achieved for 5-topic classification and 81% for 20-topic classification.

Publication DOI: https://doi.org/10.3233/SHTI220613

Data Type: null

Data Format: null

Git: None