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Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach
BACKGROUND: Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events. OBJECTIVE: This study aims to demonst...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742206/ https://www.ncbi.nlm.nih.gov/pubmed/34951601 http://dx.doi.org/10.2196/28632 |
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author | Chopard, Daphne Treder, Matthias S Corcoran, Padraig Ahmed, Nagheen Johnson, Claire Busse, Monica Spasic, Irena |
author_facet | Chopard, Daphne Treder, Matthias S Corcoran, Padraig Ahmed, Nagheen Johnson, Claire Busse, Monica Spasic, Irena |
author_sort | Chopard, Daphne |
collection | PubMed |
description | BACKGROUND: Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events. OBJECTIVE: This study aims to demonstrate the feasibility of automating the coding of adverse events described in the narrative section of the serious adverse event report forms to enable statistical analysis of the aforementioned patterns. METHODS: We used the Unified Medical Language System (UMLS) as the coding scheme, which integrates 217 source vocabularies, thus enabling coding against other relevant terminologies such as the International Classification of Diseases–10th Revision, Medical Dictionary for Regulatory Activities, and Systematized Nomenclature of Medicine). We used MetaMap, a highly configurable dictionary lookup software, to identify the mentions of the UMLS concepts. We trained a binary classifier using Bidirectional Encoder Representations from Transformers (BERT), a transformer-based language model that captures contextual relationships, to differentiate between mentions of the UMLS concepts that represented adverse events and those that did not. RESULTS: The model achieved a high F1 score of 0.8080, despite the class imbalance. This is 10.15 percent points lower than human-like performance but also 17.45 percent points higher than that of the baseline approach. CONCLUSIONS: These results confirmed that automated coding of adverse events described in the narrative section of serious adverse event reports is feasible. Once coded, adverse events can be statistically analyzed so that any correlations with the trialed medicines can be estimated in a timely fashion. |
format | Online Article Text |
id | pubmed-8742206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-87422062022-01-21 Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach Chopard, Daphne Treder, Matthias S Corcoran, Padraig Ahmed, Nagheen Johnson, Claire Busse, Monica Spasic, Irena JMIR Med Inform Original Paper BACKGROUND: Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events. OBJECTIVE: This study aims to demonstrate the feasibility of automating the coding of adverse events described in the narrative section of the serious adverse event report forms to enable statistical analysis of the aforementioned patterns. METHODS: We used the Unified Medical Language System (UMLS) as the coding scheme, which integrates 217 source vocabularies, thus enabling coding against other relevant terminologies such as the International Classification of Diseases–10th Revision, Medical Dictionary for Regulatory Activities, and Systematized Nomenclature of Medicine). We used MetaMap, a highly configurable dictionary lookup software, to identify the mentions of the UMLS concepts. We trained a binary classifier using Bidirectional Encoder Representations from Transformers (BERT), a transformer-based language model that captures contextual relationships, to differentiate between mentions of the UMLS concepts that represented adverse events and those that did not. RESULTS: The model achieved a high F1 score of 0.8080, despite the class imbalance. This is 10.15 percent points lower than human-like performance but also 17.45 percent points higher than that of the baseline approach. CONCLUSIONS: These results confirmed that automated coding of adverse events described in the narrative section of serious adverse event reports is feasible. Once coded, adverse events can be statistically analyzed so that any correlations with the trialed medicines can be estimated in a timely fashion. JMIR Publications 2021-12-24 /pmc/articles/PMC8742206/ /pubmed/34951601 http://dx.doi.org/10.2196/28632 Text en ©Daphne Chopard, Matthias S Treder, Padraig Corcoran, Nagheen Ahmed, Claire Johnson, Monica Busse, Irena Spasic. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 24.12.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Chopard, Daphne Treder, Matthias S Corcoran, Padraig Ahmed, Nagheen Johnson, Claire Busse, Monica Spasic, Irena Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach |
title | Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach |
title_full | Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach |
title_fullStr | Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach |
title_full_unstemmed | Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach |
title_short | Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach |
title_sort | text mining of adverse events in clinical trials: deep learning approach |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742206/ https://www.ncbi.nlm.nih.gov/pubmed/34951601 http://dx.doi.org/10.2196/28632 |
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