在护理环境中跟踪痴呆症患者的躁动
Tracking agitation in people living with dementia in a care environment
摘要：
躁动是痴呆症患者（PwD）痛苦的一种症状，可能会使他们和其他人处于危险之中。在长期护理（LTC）环境中，护理人员跟踪并记录这些症状，以检测居民身份是否发生变化，以评估风险，并监测对干预措施的反应。然而，这种记录可能很耗时，并且由于人员限制，可能无法观察到躁动发作。这使人们对这些评估的可靠性产生了质疑，并为技术帮助跟踪和监测痴呆症的行为症状提供了机会。在本文中，我们提出了一个为期2年的现实世界的研究结果，在痴呆症的单位，其中多模式的可穿戴设备是由$20$ PwD穿。与常用的临床记录工具一致，分析了这一大型多模态时间序列数据，以跟踪8小时护理班次中躁动发作的存在。基线分类模型（AUC=0.717）在此数据集和随后的改进（AUC= 0.779）的发展奠定了基础，自动化的过程中注释搅拌事件的护理图表。
英文摘要：
Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk. In a long term care (LTC) environment, care staff track and document these symptoms as a way to detect when there has been a change in resident status to assess risk, and to monitor for response to interventions. However, this documentation can be time-consuming, and due to staffing constraints, episodes of agitation may go unobserved. This brings into question the reliability of these assessments, and presents an opportunity for technology to help track and monitor behavioural symptoms in dementia. In this paper, we present the outcomes of a 2 year real-world study performed in a dementia unit, where a multi-modal wearable device was worn by $20$ PwD. In line with a commonly used clinical documentation tool, this large multi-modal time-series data was analyzed to track the presence of episodes of agitation in 8-hour nursing shifts. The development of a baseline classification model (AUC=0.717) on this dataset and subsequent improvement (AUC= 0.779) lays the groundwork for automating the process of annotating agitation events in nursing charts.
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。
