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

Tianchun Li ¹, Wanting Zhu ¹, Wenke Xia ¹

⁰School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Frontiers in Computational Neuroscience +

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December 31, 2024

View abstract on PubMed

Summary

A new deep learning model accurately classifies da Vinci surgical robot adverse events, improving medical device safety and patient health by analyzing reports effectively.

Area Of Science

Medical device safety
Natural Language Processing
Machine Learning in Healthcare

Background

Adverse event reporting for medical devices like the da Vinci surgical robot often suffers from incomplete and inconsistent data.
Accurate classification of these events is crucial for ensuring patient safety and improving medical device performance.

Purpose Of The Study

To enhance the accuracy of classifying adverse events associated with the da Vinci surgical robot.
To develop a robust model for predicting patient harm from adverse event reports, particularly for small datasets.

Main Methods

Developed a novel deep learning model, Bert-BiLSTM-Att_dropout, combining BERT and BiLSTM with attention and dropout mechanisms.
Applied the model to a dataset of 4,568 da Vinci surgical robot adverse event reports (2013-2023).
Evaluated model performance against baseline models like GRU, LSTM, and BiLSTM-Attention.

Main Results

The Bert-BiLSTM-Att_dropout model achieved an average F1 score of 90.15%.
Significantly outperformed traditional and attention-based baseline models in text classification accuracy.
Demonstrated effective generalization and key information capture on a small dataset.

Conclusions

The proposed model significantly improves the accuracy and usability of adverse event reporting for medical devices.
This advancement contributes to preventing medical incidents and reducing patient harm.
The findings highlight the potential of combined BERT and BiLSTM models for medical text classification tasks.

Keywords:

BERT Bert-BiLSTM deep learning intelligent classification medical device adverse events