Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia
Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia
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DOI：
10.1371/journal.pone.0209909
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发表时间：
2019-01-15
期刊：
影响因子：
3.7
通讯作者：
Barnaghi, Payam
中科院分区：
文献类型：
--
作者：
Enshaeifar, Shirin;Zoha, Ahmed;Barnaghi, Payam
痴呆症是一种神经和认知疾病，影响着全世界数百万人。在英国的任何时候，四分之一的医院病床都被痴呆症患者占用，而这些住院患者中约有22%是由于可预防的原因。在本文中，我们讨论了使用物联网（IoT）技术和家用传感设备结合机器学习技术来监测痴呆症患者的健康和福祉。这将使我们能够提供更有效的预防性护理，并减少可预防的住院人数。这项工作的一个独特方面是将环境数据与通过低成本家庭传感设备收集的生理数据相结合，以提取有关痴呆症患者在自己的家庭环境中的健康和福祉的可操作信息。我们与临床医生合作设计了我们的机器学习算法，我们专注于为现实世界开发解决方案。在我们的解决方案中，我们避免生成太多警报/警报，以防止增加监控和支持工作量。我们设计了一种算法来检测尿路感染（UTI），这是痴呆症患者住院的五大原因之一（约占英国痴呆症患者住院人数的9%）。为了开发UTI检测算法，我们使用了非负矩阵分解（NMF）技术从原始观察中提取潜在因素，并将其用于聚类和识别可能的UTI病例。此外，我们还设计了一种检测活动模式变化的算法，以识别认知能力下降或健康状况下降的早期症状，从而提供个性化和预防性的护理服务。为此，我们使用了隔离森林（iForest）技术来创建日常活动模式的整体视图。本文介绍了算法，并讨论了使用从痴呆症患者及其护理人员的试验中收集的大量真实数据的工作评估。
Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.