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

Myszewski, Joshua J.; Meyer, Patrick; Bevil, Kristin; Klesius, Lisa L.; Schroeder, Kristopher M.

doi:10.3389/fdgth.2022.878369

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…amount of positive, negative, and neutral abstracts as determined by the clinicians. 4 It is important to note that on an individual article level, GAN-BioBERT only categories abstracts as positive, negative, or neutral. This three-category classification scheme was chosen to provide adequate detail without sacrificing an adequate level of accuracy.…”

Section: Negativementioning

confidence: 99%

“…3 Furthermore, specific algorithms have been developed that facilitate sentiment analysis of clinical trial abstracts. 4 This presents an intriguing avenue for quick assessment of the qualitative statements made by the authors of a study. However, sentiment analysis as an adjunct or complement to other methods of systemic analysis of biomedical literature has not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery

Myszewski

Schroeder

2021

Reg Anesth Pain Med

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IntroductionSentiment analysis, by evaluating written wording and its context, is a growing tool used in computer science that can determine the level of support expressed in a body of text using artificial intelligence methodologies. The application of sentiment analysis to biomedical literature is a growing field and offers the potential to rapidly and economically explore large amounts of published research and characterize treatment efficacy.MethodsWe compared the results of sentiment analysis of 115 article abstracts analyzed in a recently published meta-analysis of peripheral nerve block usage in primary hip and knee arthroplasty to the conclusions drawn by the authors of the original meta-analysis.ResultsA moderately positive outlook supporting the utilization of regional anesthesia for hip and knee arthroplasty was found in the 115 articles that were included for analysis, with 46% expressing positive sentiment, 35% expressing neutral sentiment, and 19% of abstracts expressing negative sentiment. This was well aligned with the conclusions reached by a previous meta-analysis of the same articles.DiscussionSentiment analysis applied to the medical literature can rapidly evaluate large collections of published data and generate an impression of overall findings that are aligned with the findings of a traditional meta-analysis.

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Section: Negativementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery

Myszewski

Schroeder

2021

Reg Anesth Pain Med

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“…In the study of Myszewski et al ( 2022 ), the authors showed that the combination of a GAN-BERT setting with a domain-specific PLM BioBERT (Lee et al, 2019 ) outperforms the original GAN-BERT on a sentiment classification task for clinical trial abstracts. However, the authors do not compare the results with those of PLM-only classification.…”

Section: Introductionmentioning

confidence: 99%

Improving text mining in plant health domain with GAN and/or pre-trained language model

Jiang

Cormier

Angarita

et al. 2023

Front. Artif. Intell.

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The Bidirectional Encoder Representations from Transformers (BERT) architecture offers a cutting-edge approach to Natural Language Processing. It involves two steps: 1) pre-training a language model to extract contextualized features and 2) fine-tuning for specific downstream tasks. Although pre-trained language models (PLMs) have been successful in various text-mining applications, challenges remain, particularly in areas with limited labeled data such as plant health hazard detection from individuals' observations. To address this challenge, we propose to combine GAN-BERT, a model that extends the fine-tuning process with unlabeled data through a Generative Adversarial Network (GAN), with ChouBERT, a domain-specific PLM. Our results show that GAN-BERT outperforms traditional fine-tuning in multiple text classification tasks. In this paper, we examine the impact of further pre-training on the GAN-BERT model. We experiment with different hyper parameters to determine the best combination of models and fine-tuning parameters. Our findings suggest that the combination of GAN and ChouBERT can enhance the generalizability of the text classifier but may also lead to increased instability during training. Finally, we provide recommendations to mitigate these instabilities.

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Contextual Word Embedding for Biomedical Knowledge Extraction: a Rapid Review and Case Study

Vithanage,

Yu,

Wang

et al. 2024

J Healthc Inform Res

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

Cited by 3 publications

References 19 publications

Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery

Evaluation of language analysis to summarize the literature: a comparison to traditional meta-analysis in primary hip and knee surgery

Improving text mining in plant health domain with GAN and/or pre-trained language model

Contextual Word Embedding for Biomedical Knowledge Extraction: a Rapid Review and Case Study

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