Shi Meng, Zheng Yu, Wu Youzhen, Ren Quansheng
School of Electronics, Peking University, Beijing 100871, China.
College of Engineering, Peking University, Beijing 100871, China.
Bioengineering (Basel). 2023 Aug 31;10(9):1026. doi: 10.3390/bioengineering10091026.
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.
手术过程中及时检测并应对术中低血压（IOH）对于避免严重的术后并发症至关重要。尽管已经提出了几种使用机器学习预测IOH的方法，但其性能仍有提升空间。在本文中，我们提出了一种基于多任务训练和注意力机制的ResNet-BiLSTM模型用于IOH预测。我们使用从非心脏手术患者监测中获得的生物信号波形对我们提出的模型进行训练和测试。我们选择了三种处理时间序列数据的模型（WaveNet、CNN和TCN）进行比较。实验结果表明，与其他模型相比，我们提出的模型具有最优的均方误差（MSE，43.83）和准确率（0.9224），包括WaveNet（51.52，0.9087）、CNN（318.52，0.5861）和TCN（62.31，0.9045），这表明我们提出的模型具有更好的回归和分类性能。我们对多任务和注意力机制进行了消融实验，实验结果表明多任务和注意力机制提高了MSE和准确率。结果证明了我们提出的模型在预测IOH方面的有效性和优越性。
