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Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing
The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638042/ https://www.ncbi.nlm.nih.gov/pubmed/37954315 http://dx.doi.org/10.1016/j.heliyon.2023.e21723 |
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author | Luschi, Alessio Nesi, Paolo Iadanza, Ernesto |
author_facet | Luschi, Alessio Nesi, Paolo Iadanza, Ernesto |
author_sort | Luschi, Alessio |
collection | PubMed |
description | The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts. |
format | Online Article Text |
id | pubmed-10638042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106380422023-11-11 Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing Luschi, Alessio Nesi, Paolo Iadanza, Ernesto Heliyon Research Article The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts. Elsevier 2023-10-31 /pmc/articles/PMC10638042/ /pubmed/37954315 http://dx.doi.org/10.1016/j.heliyon.2023.e21723 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Luschi, Alessio Nesi, Paolo Iadanza, Ernesto Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing |
title | Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing |
title_full | Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing |
title_fullStr | Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing |
title_full_unstemmed | Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing |
title_short | Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing |
title_sort | evidence-based clinical engineering: health information technology adverse events identification and classification with natural language processing |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638042/ https://www.ncbi.nlm.nih.gov/pubmed/37954315 http://dx.doi.org/10.1016/j.heliyon.2023.e21723 |
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