Leveraging natural language processing to aggregate field safety notices of medical devices across the EU

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.

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Publication Details

Journal
npj Digital Medicine
Published
2024-12-04
DOI
https://doi.org/10.1038/s41746-024-01337-9
Citations
5
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
1.17

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article

Leveraging natural language processing to aggregate field safety notices of medical devices across the EU

Enrico G. Caiani, Yijun Ren
5 citations
npj Digital Medicine
Biomedical Text Mining and Ontologies
1.17
article

Leveraging natural language processing to aggregate field safety notices of medical devices across the EU

Enrico G. Caiani, Yijun Ren
article en
5 citations

Abstract

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.

npj Digital MedicineVol. 7(1)
IRCCS Istituto Auxologico Italiano (IT), Politecnico di Milano (IT)
European Commission, Horizon 2020 Framework Programme
Quality Education
Openalex Percentile: Top 21%
Biomedical Text Mining and Ontologies
1.17
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