Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach

TL;DR

This paper presents a novel hybrid rule-based and deep learning method for extracting drug-related information from French clinical documents, addressing privacy concerns and practical deployment challenges.

Contribution

It introduces a new approach tailored for French clinical texts, combining rule-based and deep learning techniques, and demonstrates its effectiveness in real-world health data management.

Findings

Outperforms existing methods in extracting drug information

Successfully deployed in a health data platform

Enhances structuring of French clinical documents

Abstract

Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a health data management platform where it is used to structure drug medical data and help patients organize their drug schedules. It can be implemented on any web or mobile platform. This work closes the gap between theoretical and practical work by creating an application adapted to real production problems. It is a combination of a rule-based phase and a Deep Learning approach. Finally, numerical results show the outperformance and relevance of the proposed methodology.