Ren Yijun, Caiani Enrico Gianluca
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
IRCCS Istituto Auxologico Italiano, Ospedale S. Luca, Milan, Italy.
NPJ Digit Med. 2024 Dec 4;7(1):352. doi: 10.1038/s41746-024-01337-9.
The European Union (EU) Medical Device Regulation and In Vitro Medical Device Regulation have introduced more rigorous regulatory requirements for medical devices, including new rules for post-market surveillance. However, EU market vigilance is limited by the absence of harmonized reporting systems, languages and nomenclatures among Member States. Our aim was to develop a framework based on Natural Language Processing capable of automatically collecting publicly available Field Safety Notices (FSNs) reporting medical device problems by applying web scraping to EU authority websites, to attribute the most suitable device category based on the European Medical Device Nomenclature (EMDN), and to display processed FSNs in an aggregated way to allow multiple queries. 65,036 FSNs published up to 31/12/2023 were retrieved from 16 EU countries, of which 40,212 (61.83%) were successfully assigned the proper EMDN. The framework's performance was successfully tested, with accuracies ranging from 87.34% to 98.71% for EMDN level 1 and from 64.15% to 85.71% even for level 4.
欧盟医疗器械法规和体外诊断医疗器械法规对医疗器械提出了更严格的监管要求，包括上市后监督的新规则。然而，欧盟市场监管因成员国之间缺乏统一的报告系统、语言和术语而受到限制。我们的目标是开发一个基于自然语言处理的框架，该框架能够通过对欧盟官方网站进行网络抓取，自动收集公开可用的、报告医疗器械问题的现场安全通知（FSN），根据欧洲医疗器械命名法（EMDN）确定最合适的器械类别，并以汇总的方式显示处理后的FSN，以便进行多次查询。从16个欧盟国家检索到截至2023年12月31日发布的65,036份FSN，其中40,212份（61.83%）成功分配了正确的EMDN。该框架的性能得到了成功测试，EMDN 1级的准确率在87.34%至98.71%之间，即使是4级的准确率也在64.15%至85.71%之间。
