Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

Abstract

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.

Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review

“…Geriatric patients are also prone to inadequate nutritional intake because of factors, including polypharmacy, decreased mobility, and physiological changes. 15 Often such patients experience deterioration of mental 16 or physical health during their hospital stay, even if they recover from the primary chief of concern for the admission. 17 Studies have also shown that hospitalized geriatric patients have a 60-fold increased risk of developing permanent disabilities 18 making them more susceptible to other adverse ailments.…”

“… 74 Screening tools for dementia are being developed for detecting early cognitive impairments 75–79 and other geriatric health problems such as fall risks 80 and urinary tract infections among patients with dementia. 16 …”

Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study

“…Second, the system provides the community staff with detailed information regarding each resident’s activities and behavior. Changes in an adult’s activity and behavior are well-characterized to precede health declines; therefore, staff can use this information to quickly identify older adults that are at an increased probability for a health decline and intervene much earlier [ 49 , 50 ]. For example, such information can be used to identify and flag older adults that show the earliest sign of a urinary tract infection (UTI).…”

“…For example, such information can be used to identify and flag older adults that show the earliest sign of a urinary tract infection (UTI). In particular, the CarePredict system identifies a new or marked increase in urination urgency or frequency, both well-established indicators for a symptomatic UTI [ 49 - 51 ]. These older adults can then be assessed, and if diagnosed with a UTI, can be provided antibiotics to treat the UTI before it may result in hospitalization [ 51 ].…”

Proactive and Integrated Management and Empowerment in Parkinson’s Disease: Designing a New Model of Care

“…Emerging evidence supports this approach. A recent study demonstrated the value of home sensing devices for early detection of urinary tract infections in people with dementia [44].…”