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New AI Could Detect and Reduce Urinary Tract Infections in Dementia Patients

New AI Could Detect and Reduce Urinary Tract Infections in Dementia Patients

The University of Surrey has developed a novel AI that has the potential to detect and reduce urinary tract infections (UTI)—one of the leading causes of hospitalization for dementia patients.

Image credit: University of Surrey

UTI is a type of infection that can occur on any part of the urinary system, from the bladder to the kidneys. Symptoms of UTI include blood in urine, pain in the lower area of the stomach, mood and behavioral changes, and the urge to urinate quickly or more often than usual.

In a study reported in PLOS One, researchers from the University of Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) described how they utilized a method, termed non-negative matrix factorization, in an NHS clinical trial to identify the hidden clues of potential UTI cases. The researchers subsequently applied innovative machine learning algorithms to detect early UTI symptoms.

The latest experiment was part of the Technology Integrated Health Management (TIHM) for dementia project, headed by Surrey and Borders Partnership NHS Foundation Trust and in association with industry collaborators and the University of Surrey. Part of the NHS Test Beds Programme and supported by NHS England and the Office for Life Sciences, the project enabled clinicians to remotely track the health of dementia patients living at home, with the aid of a network of internet-enabled devices like vital body signal monitoring devices and environmental and activity monitoring sensors. Through machine learning solutions, data streamed from these devices were examined and the health problems, thus detected, were flagged on a digital dashboard and these were subsequently followed up by a clinical monitoring group.

The World Health Organization has revealed that about 50 million people across the world suffer from dementia. It has been estimated that this number will reach 82 million by 2030 and 152 million by 2050. Similarly, the Alzheimer’s Society reported that one in four hospital beds in the United Kingdom are occupied by a dementia patient, while about 22% of these admissions are believed to be preventable.

Urinary tract infections are one of the most common reasons why people living with dementia go into hospital. We have developed a tool that is able to identify the risk of UTIs so it is then possible to treat them early. We are confident our algorithm will be a valuable tool for healthcare professionals, allowing them to produce more effective and personalised plans for patients.

Payam Barnaghi, Professor of Machine Intelligence, CVSSP, University of Surrey

This development hints at the incredible potential of Professor Barnaghi’s research here at CVSSP. Machine learning could provide improved care for people living with dementia to remain at home, reducing hospitalisation and helping the NHS to free up bed space.

Adrian Hilton, Professor, Director, CVSSP, University of Surrey

I am delighted to see that the algorithms we have designed have an impact on improving the healthcare of people with dementia and providing a tool for clinicians to offer better support to their patients.

Dr Shirin Enshaeifar, Senior Research Fellow, CVSSP, University of Surrey

The TIHM for dementia study is a collaborative project that has brought together the NHS, academia and industry to transform support for people with dementia living at home and their carers. Our aim has been to create an Internet of Things led system that uses machine learning to alert our clinicians to potential health problems that we can step in and treat early. The system helps to improve the lives of people with dementia and their carers and could also reduce pressure on the NHS.

Helen Rostill, Professor, Director of Innovation and Development, Surrey and Borders Partnership NHS Foundation Trust

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