用深度学习预测呼吸机脱机成功率，准确率达98%
Development of a Deep Learning Model for the Prediction of Ventilator Weaning
- 用卷积神经网络分析呼吸流和心电数据，结合时频分析
- 自研CNN模型在WEANDB数据集上达到98%平均准确率
- 适合重症监护室医生快速决策，降低脱机失败风险
呼吸机脱机失败是重症监护室中的关键问题，约20%的患者在撤机后48小时内出现自主呼吸困难或气道不畅，可导致临床进程恶化及死亡率上升25至50。为此，本文提出一种基于卷积神经网络（CNN）的医疗辅助系统，用于评估患者在自发呼吸试验（SBT）后是否适合撤除机械通气。在SBT过程中记录呼吸流与心电活动，并通过时频分析（TFA）处理。研究对比了两种CNN架构：基于ResNet50且经贝叶斯优化调参的模型，以及从头设计并同样使用贝叶斯优化调整结构的模型。实验采用WEANDB数据库进行训练与评估。结果显示，自研CNN模型平均准确率达98%，具有重要临床意义，能为重症监护医生提供可靠工具，提升撤机决策的及时性与准确性，从而降低脱机失败带来的不良后果。
原文摘要 · Abstract (English)
The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20 of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25 to 50. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patients suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.
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