Urinary tract infections (UTIs) are one of the most common causes of hospitalisation, especially for people living with dementia. As with most conditions, spotting it early can help to limit its impact. A team from the University of Surrey believe an AI-based system they’ve developed can do just that.
The research, which was documented in a recently published paper, resulted in a system based upon non-negative matrix factorisation was able to spot hidden clues of potential UTIs in an NHS clinical trial.
The work involved the remote monitoring of people with dementia as they lived at home. A number of internet enabled devices streamed back data, which was used to train and power a machine learning system that was able to provide an early warning of potential health problems to a clinical team.
Early detection
“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,” the researchers say.
With millions of people living with dementia, it’s vital that technology is developed to help them retain independence whilst remaining as healthy as possible. The team believe that machine learning could prove vital in helping those with dementia stay at home whilst reducing their hospitalization frequency.
“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,” they conclude.
