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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1476164
This article is part of the Research Topic The Convergence of AI, LLMs, and Industry 4.0: Enhancing BCI, HMI, and Neuroscience Research View all articles

Research on adverse event classification algorithm of da Vinci surgical robot based on Bert-BiLSTM model

Provisionally accepted
Tianchun Li Tianchun Li 1Wanting Zhu Wanting Zhu 1*Wenke Xia Wenke Xia 1*Li Wang Li Wang 2*Weiqi Li Weiqi Li 1*Peiming Zhang Peiming Zhang 1*
  • 1 School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
  • 2 Henan Center for Drug Evaluation and Inspection, Zhengzhou, Henan Province, China

The final, formatted version of the article will be published soon.

    This study aims to enhance the classification accuracy of adverse events associated with the Da Vinci surgical robot through advanced natural language processing techniques, thereby ensuring medical device safety and protecting patient health. Addressing the issues of incomplete and inconsistent adverse event records, we employed a deep learning model that combines BERT and BiLSTM to predict whether adverse event reports resulted in patient harm. We developed the Bert-BiLSTM-Att_dropout model specifically for text classification tasks with small datasets, optimizing the model's generalization ability and key information capture through the integration of dropout and attention mechanisms.Our model demonstrated exceptional performance on a dataset comprising 4,568 Da Vinci surgical robot adverse event reports collected from 2013 to 2023, achieving an average F1 score of 90.15%, significantly surpassing baseline models such as GRU, LSTM, BiLSTM-Attention, and BERT. This achievement not only validates the model's effectiveness in text classification within this specific domain but also substantially improves the usability and accuracy of adverse event reporting, contributing to the prevention of medical incidents and reduction of patient harm.Furthermore, our research experimentally confirmed the model's performance, alleviating the data classification and analysis burden for healthcare professionals. Through comparative analysis, we highlighted the potential of combining BERT and BiLSTM in text classification tasks, particularly for small datasets in the medical field. Our findings advance the development of adverse event monitoring technologies for medical devices and provide critical insights for future research and enhancements.

    Keywords: Medical device adverse events, Bert-BiLSTM, deep learning, Intelligent classification, BERT

    Received: 26 Sep 2024; Accepted: 22 Nov 2024.

    Copyright: © 2024 Li, Zhu, Xia, Wang, Li and Zhang. 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) or licensor 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.

    * Correspondence:
    Wanting Zhu, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
    Wenke Xia, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
    Li Wang, Henan Center for Drug Evaluation and Inspection, Zhengzhou, Henan Province, China
    Weiqi Li, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
    Peiming Zhang, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.