Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials.

Autor:	Myszewski JJ; School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States., Klossowski E; University of Wisconsin-Milwaukee, Milwaukee, WI, United States., Meyer P; Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States., Bevil K; Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States., Klesius L; Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States., Schroeder KM; Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2022 May 24; Vol. 4, pp. 878369. Date of Electronic Publication: 2022 May 24 (Print Publication: 2022).
DOI:	10.3389/fdgth.2022.878369
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Myszewski, Klossowski, Meyer, Bevil, Klesius and Schroeder.)
Databáze:	MEDLINE
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