不要喊狼来了：重症监护中的远程监督多任务学习
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
摘要：
重症监护室（ICU）的患者需要持续和密切的监护。为了帮助临床工作人员完成这项任务，医院使用监控系统，如果其算法指示患者的状况可能正在恶化，则会触发视听警报。然而，目前的监测系统对运动伪影和技术错误极为敏感。因此，它们通常每天为每位患者触发数百到数千个假警报-将重要警报淹没在噪音中，并增加临床工作人员的疲惫。在这种情况下，数据是丰富的，但获得值得信赖的专家注释是费力和昂贵的。我们将从多变量时间序列中减少虚警的问题框架为机器学习任务，并使用一种新型的多任务网络架构来解决这个问题，该架构通过多个相关的辅助任务进行远程监督，以减少训练所需的昂贵标签的数量。我们表明，我们的方法比现实世界ICU数据的几个最先进的基线有了显着的改进，并为远程监督多任务学习中任务选择和架构选择的重要性提供了新的见解。
英文摘要：
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive to movement artefacts and technical errors. As a result, they typically trigger hundreds to thousands of false alarms per patient per day - drowning the important alarms in noise and adding to the exhaustion of clinical staff. In this setting, data is abundantly available, but obtaining trustworthy annotations by experts is laborious and expensive. We frame the problem of false alarm reduction from multivariate time series as a machine-learning task and address it with a novel multitask network architecture that utilises distant supervision through multiple related auxiliary tasks in order to reduce the number of expensive labels required for training. We show that our approach leads to significant improvements over several state-of-the-art baselines on real-world ICU data and provide new insights on the importance of task selection and architectural choices in distantly supervised multitask learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
