基于传感器的痴呆症住院患者躁动预测系统综述

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jan Kleine Deters , Sarah Janus , Jair A. Lima Silva , Heinrich J. Wörtche , Sytse U. Zuidema
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引用次数: 0

摘要

及早发现痴呆症患者的躁动,可以及时采取干预措施,防止情况恶化,提高他们的生活质量。多模态传感技术的出现和人工智能的进步使得探索和应用技术来实现这一目标成为可能。我们进行了一次文献综述,以了解当前的技术发展及其在护理机构中的应用挑战。我们的系统性综述使用了 Pubmed 和 IEEE 科学数据库,考虑了 2017 年以来的研究。我们纳入的研究侧重于将传感器数据与躁动的声音和/或身体表现联系起来。在1622项已确定的研究中,有12项被选中进行最终审查。我们对研究设计、技术、决策数据和数据分析进行了分析。我们发现在行为描述和系统事件生成配置的标准化语义表述方面存在差距。这项研究强调了利用护理人员日常工作中现有信息的举措,例如将电子健康记录与传感器数据相关联。随着预测系统越来越多地集成到护理日常工作中,需要解决减少误报的问题,因为误报会阻碍系统的采用。因此,为了确保自适应预测能力和个性化系统重新配置,我们建议在未来的工作中评估一个框架,该框架将人在环中的方法纳入到躁动的检测和预测中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor-based agitation prediction in institutionalized people with dementia A systematic review

Early detection of agitation in individuals with dementia can lead to timely interventions, preventing the worsening of situations and enhancing their quality of life. The emergence of multi-modal sensing and advances in artificial intelligence make it feasible to explore and apply technology for this goal. We conducted a literature review to understand the current technical developments and challenges of its integration in caregiving institutions. Our systematic review used the Pubmed and IEEE scientific databases, considering studies from 2017 onwards. We included studies focusing on linking sensor data to vocal and/or physical manifestations of agitation. Out of 1622 identified studies, 12 were selected for the final review. Analysis was conducted on study design, technology, decisional data, and data analytics. We identified a gap in the standardized semantic representation of both behavioral descriptions and system event generation configurations. This research highlighted initiatives that leverage existing information in a caregiver's routine, such as correlating electronic health records with sensor data. As predictive systems become more integrated into caregiving routines, false positive reduction needs to be addressed as those will discourage their adoption. Therefore, to ensure adaptive predictive capacity and personalized system re-configuration, we suggest future work to evaluate a framework that incorporates a human-in-the-loop approach for detecting and predicting agitation.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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