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21 results on '"Rostill, Helen"'

2. Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia

Author

Li, Honglin, Kolanko, Magdalena Anita, Enshaeifar, Shirin, Skillman, Severin, Markides, Andreas, Kenny, Mark, Soreq, Eyal, Kouchaki, Samaneh, Jensen, Kirsten, Cameron, Loren, Crone, Michael, Freemont, Paul, Rostill, Helen, Sharp, David J., Nilforooshan, Ramin, and Barnaghi, Payam

Subjects
Computer Science - Machine Learning
Abstract

Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia. However, accessing sufficient labelled training samples and integrating high-quality, routinely collected data from heterogeneous in-home monitoring technologies are main obstacles hindered utilising these technologies in real-world medicine. This work presents a semi-supervised model that can continuously learn from routinely collected in-home observation and measurement data. We show how our model can process highly imbalanced and dynamic data to make robust predictions in analysing the risk of Urinary Tract Infections (UTIs) in dementia. UTIs are common in older adults and constitute one of the main causes of avoidable hospital admissions in people with dementia (PwD). Health-related conditions, such as UTI, have a lower prevalence in individuals, which classifies them as sporadic cases (i.e. rare or scattered, yet important events). This limits the access to sufficient training data, without which the supervised learning models risk becoming overfitted or biased. We introduce a probabilistic semi-supervised learning framework to address these issues. The proposed method produces a risk analysis score for UTIs using routinely collected data by in-home sensing technologies., Comment: 11 pages

Published
2020

4. Remote Monitoring of Physiology in People Living With Dementia: An Observational Cohort Study

Author

David, Michael C B, primary, Kolanko, Magdalena, additional, Del Giovane, Martina, additional, Lai, Helen, additional, True, Jessica, additional, Beal, Emily, additional, Li, Lucia M, additional, Nilforooshan, Ramin, additional, Barnaghi, Payam, additional, Malhotra, Paresh A, additional, Rostill, Helen, additional, Wingfield, David, additional, Wilson, Danielle, additional, Daniels, Sarah, additional, Sharp, David J, additional, and Scott, Gregory, additional

Published
2023
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15. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

Author

Enshaeifar, Shirin, primary, Zoha, Ahmed, additional, Markides, Andreas, additional, Skillman, Severin, additional, Acton, Sahr Thomas, additional, Elsaleh, Tarek, additional, Hassanpour, Masoud, additional, Ahrabian, Alireza, additional, Kenny, Mark, additional, Klein, Stuart, additional, Rostill, Helen, additional, Nilforooshan, Ramin, additional, and Barnaghi, Payam, additional

Published
2018
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16. The Internet of Things for Dementia Care

Author

Enshaeifar, Shirin, primary, Barnaghi, Payam, additional, Skillman, Severin, additional, Markides, Andreas, additional, Elsaleh, Tarek, additional, Acton, Sahr Thomas, additional, Nilforooshan, Ramin, additional, and Rostill, Helen, additional

Published
2018
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17. A CLOSE EYE ON DEMENTIA.

Author

Rostill, Helen

Subjects
ALGORITHMS, ARTIFICIAL intelligence, COMMUNITY health services, DEMENTIA patients, MACHINE learning, SERVICES for caregivers, EVALUATION of medical care, NATIONAL health services, QUALITY of life, TELEMEDICINE, HOME environment, EARLY medical intervention, INTERNET of things
Abstract

The article discusses artificial intelligence's monitoring of dementia-stricken people at home to lessen hospital admissions and engage more carers in Great Britain. Topics covered include the Technology Integrated Health Management (TIHM) for Dementia trial's data collection of a person's health and environment via sensors and monitors, and its analysis and identification of any health changes. Also noted is the trial's reporting of a reduction in such symptoms as depression and agitation.

Published
2019

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