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

• Published in: PloS one Vol. 14; no. 1; p. e0209909
• Main Authors: Enshaeifar, Shirin, Zoha, Ahmed, Skillman, Severin, Markides, Andreas, Acton, Sahr Thomas, Elsaleh, Tarek, Kenny, Mark, Rostill, Helen, Nilforooshan, Ramin, Barnaghi, Payam
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.01.2019; Public Library of Science (PLoS)
• Subjects: Activities of Daily Living; Activity patterns; Aged; Algorithms; Artificial intelligence; Biology and Life Sciences; Change detection; Clustering; Cognitive ability; Computer and Information Sciences; Dementia; Dementia - physiopathology; Dementia - therapy; Dementia disorders; Diagnosis; Elderly patients; Engineering; Female; Health; Health aspects; Home environment; Hospitalization; Hospitals; Humans; Infections; Information processing; Intensive care; Internet of Things; Learning algorithms; Machine Learning; Male; Medicine; Medicine and Health Sciences; Middle Aged; Physical Sciences; Prevention; Research and Analysis Methods; Risk factors; Signal processing; Sleep disorders; Speech; United Kingdom; Urinary tract; Urinary tract diseases; Urinary tract infections; Urinary Tract Infections - diagnosis; Urinary Tract Infections - physiopathology; Urinary Tract Infections - therapy; Urogenital system; United Kingdom; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0209909

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.