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Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials
BACKGROUND: The aim of this study was to validate a three-class sentiment classification model for clinical trial abstracts combining adversarial learning and the BioBERT language processing model as a tool to assess trends in biomedical literature in a clearly reproducible manner. We then assessed...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170913/ https://www.ncbi.nlm.nih.gov/pubmed/35685304 http://dx.doi.org/10.3389/fdgth.2022.878369 |
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author | Myszewski, Joshua J. Klossowski, Emily Meyer, Patrick Bevil, Kristin Klesius, Lisa Schroeder, Kristopher M. |
author_facet | Myszewski, Joshua J. Klossowski, Emily Meyer, Patrick Bevil, Kristin Klesius, Lisa Schroeder, Kristopher M. |
author_sort | Myszewski, Joshua J. |
collection | PubMed |
description | BACKGROUND: The aim of this study was to validate a three-class sentiment classification model for clinical trial abstracts combining adversarial learning and the BioBERT language processing model as a tool to assess trends in biomedical literature in a clearly reproducible manner. We then assessed the model's performance for this application and compared it to previous models used for this task. METHODS: Using 108 expert-annotated clinical trial abstracts and 2,000 unlabeled abstracts this study develops a three-class sentiment classification algorithm for clinical trial abstracts. The model uses a semi-supervised model based on the Bidirectional Encoder Representation from Transformers (BERT) model, a much more advanced and accurate method compared to previously used models based upon traditional machine learning methods. The prediction performance was compared to those previous studies. RESULTS: The algorithm was found to have a classification accuracy of 91.3%, with a macro F1-Score of 0.92, significantly outperforming previous studies used to classify sentiment in clinical trial literature, while also making the sentiment classification finer grained with greater reproducibility. CONCLUSION: We demonstrate an easily applied sentiment classification model for clinical trial abstracts that significantly outperforms previous models with greater reproducibility and applicability to large-scale study of reporting trends. |
format | Online Article Text |
id | pubmed-9170913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91709132022-06-08 Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials Myszewski, Joshua J. Klossowski, Emily Meyer, Patrick Bevil, Kristin Klesius, Lisa Schroeder, Kristopher M. Front Digit Health Digital Health BACKGROUND: The aim of this study was to validate a three-class sentiment classification model for clinical trial abstracts combining adversarial learning and the BioBERT language processing model as a tool to assess trends in biomedical literature in a clearly reproducible manner. We then assessed the model's performance for this application and compared it to previous models used for this task. METHODS: Using 108 expert-annotated clinical trial abstracts and 2,000 unlabeled abstracts this study develops a three-class sentiment classification algorithm for clinical trial abstracts. The model uses a semi-supervised model based on the Bidirectional Encoder Representation from Transformers (BERT) model, a much more advanced and accurate method compared to previously used models based upon traditional machine learning methods. The prediction performance was compared to those previous studies. RESULTS: The algorithm was found to have a classification accuracy of 91.3%, with a macro F1-Score of 0.92, significantly outperforming previous studies used to classify sentiment in clinical trial literature, while also making the sentiment classification finer grained with greater reproducibility. CONCLUSION: We demonstrate an easily applied sentiment classification model for clinical trial abstracts that significantly outperforms previous models with greater reproducibility and applicability to large-scale study of reporting trends. Frontiers Media S.A. 2022-05-24 /pmc/articles/PMC9170913/ /pubmed/35685304 http://dx.doi.org/10.3389/fdgth.2022.878369 Text en Copyright © 2022 Myszewski, Klossowski, Meyer, Bevil, Klesius and Schroeder. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Myszewski, Joshua J. Klossowski, Emily Meyer, Patrick Bevil, Kristin Klesius, Lisa Schroeder, Kristopher M. Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials |
title | Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials |
title_full | Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials |
title_fullStr | Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials |
title_full_unstemmed | Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials |
title_short | Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials |
title_sort | validating gan-biobert: a methodology for assessing reporting trends in clinical trials |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170913/ https://www.ncbi.nlm.nih.gov/pubmed/35685304 http://dx.doi.org/10.3389/fdgth.2022.878369 |
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