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.
翻译:法国医疗数据结构化仍是一个挑战,主要原因是隐私问题导致医疗数据缺乏,处理法语的方法和方法也缺乏,这些挑战之一是法国临床文件中与毒品有关的信息结构化。据我们所知,过去十年来,研究法国处方的相关文件不到5份。本文件提出了从法国临床扫描文件中提取与毒品有关的信息的新办法,同时保护病人的隐私。此外,我们还在健康数据管理平台中运用了我们的方法,用于构建药物医疗数据,帮助病人组织药物计划。可以在任何网络或移动平台上实施。这项工作通过创建适应实际生产问题的应用软件,缩小理论工作与实际工作之间的差距。这是一个基于规则的阶段和深入学习方法的结合。最后,数字结果显示了拟议方法的绩效和相关性。