首页|University of Virginia Researcher Adds New Data to Research in Machine Learning (Enhancing Literature Review Efficiency: A Case Study on Using Fine-Tuned BERT f or Classifying Focused Ultrasound-Related Articles)
University of Virginia Researcher Adds New Data to Research in Machine Learning (Enhancing Literature Review Efficiency: A Case Study on Using Fine-Tuned BERT f or Classifying Focused Ultrasound-Related Articles)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting originating from Charlottesvi lle, Virginia, by NewsRx correspondents, research stated, "Over the past decade, focused ultrasound (FUS) has emerged as a promising therapeutic modality for va rious medical conditions." Funders for this research include Focused Ultrasound Foundation, Charlottesville , Virginia. Our news correspondents obtained a quote from the research from University of Vi rginia: "However, the exponential growth in the published literature on FUS ther apies has made the literature review process increasingly time-consuming, ineffi cient, and error-prone. Machine learning approaches offer a promising solution t o address these chAllenges. Therefore, the purpose of our study is to (1) explor e and compare machine learning techniques for the text classification of scienti fic abstracts, and (2) integrate these machine learning techniques into the conv entional literature review process. A classified dataset of 3588 scientific abst racts related and unrelated to FUS therapies sourced from the PubMed database wa s used to train various traditional machine learning and deep learning models. the fine-tuned Bio-ClinicalBERT (Bidirectional Encoder Representations from Trans formers) model, which we named FusBERT, had comparatively optimal performance me trics with an accuracy of 0.91, a precision of 0.85, a recAll of 0.99, and an F1 of 0.91."
University of VirginiaCharlottesvilleVirginiaUnited StatesNorth and Central AmericaCyborgsEmerging Technolog iesMachine Learning
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