Key result
A proposed ResNet-BiLSTM model with multitask training and attention mechanisms achieved optimal accuracy (0.9224) and MSE (43.83) for predicting intraoperative hypotension compared to other models.
Why the study?
Timely detection of intraoperative hypotension is crucial to avoid severe complications, but current machine learning prediction methods have space for improvement.
Does a ResNet-BiLSTM model based on multitask training and attention mechanism improve the prediction of intraoperative hypotension compared to other machine learning models in patients undergoing non-cardiac surgery?
Does a ResNet-BiLSTM model based on multitask training and attention mechanism improve the prediction of intraoperative hypotension compared to other machine learning models in patients undergoing non-cardiac surgery?
A novel ResNet-BiLSTM model with multitask training and attention mechanisms outperforms standard time-series models in predicting intraoperative hypotension during non-cardiac surgery.
Answers are grounded in this paper and cite their sources.
May support ML-based intraoperative hypotension prediction; leaves open prospective validation before clinical use.
Timely detection and response to Intraoperative Hypotension (IOH) during surgery is crucial to avoid severe postoperative complications. Although several methods have been proposed to predict IOH using machine learning, their performance still has space for improvement. In this paper, we propose a ResNet-BiLSTM model based on multitask training and attention mechanism for IOH prediction. We trained and tested our proposed model using bio-signal waveforms obtained from patient monitoring of non-cardiac surgery. We selected three models (WaveNet, CNN, and TCN) that process time-series data for comparison. The experimental results demonstrate that our proposed model has optimal MSE (43.83) and accuracy (0.9224) compared to other models, including WaveNet (51.52, 0.9087), CNN (318.52, 0.5861), and TCN (62.31, 0.9045), which suggests that our proposed model has better regression and classification performance. We conducted ablation experiments on the multitask and attention mechanisms, and the experimental results demonstrated that the multitask and attention mechanisms improved MSE and accuracy. The results demonstrate the effectiveness and superiority of our proposed model in predicting IOH.
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Shi et al. (2023) studied Intraoperative Hypotension (IOH). ResNet-BiLSTM model based on multitask training and attention mechanism vs. WaveNet, CNN, and TCN models was evaluated on Mean Squared Error (MSE) and accuracy for IOH prediction. A proposed ResNet-BiLSTM model with multitask training and attention mechanisms achieved optimal accuracy (0.9224) and MSE (43.83) for predicting intraoperative hypotension compared to other models.
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