Urinary tract infections (UTI) is an infection of any part of the urinary system, from the kidneys to the bladder. The symptoms include pain in the lower part of the stomach, blood in urine, needing to urinate suddenly or more often than usual, and changes in mood and behaviour.

In an NHS clinical trial, scientists from the University of Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP) used a technique called Non-negative Matrix Factorisation to find hidden clues of possible UTI cases. The team then used novel machine learning algorithms to identify early UTI symptoms.

The experiment was part of the TIHM (Technology Integrated Health Management) for dementia project, led by Surrey and Borders Partnership NHS Foundation Trust and in partnership with the University of Surrey and industry collaborators. The project, which is part of the NHS Test Beds Programme and is funded by NHS England the Office for Life Sciences, allowed clinicians to remotely monitor the health of people with dementia living at home, with the help of a network of internet enabled devices such as environmental and activity monitoring sensors, and vital body signal monitoring devices. Data streamed from these devices was analysed using machine learning solutions, and the identified health problems were flagged on a digital dashboard and followed up by a clinical monitoring team.

According to The World Health Organisation, around 50 million people worldwide have dementia. This number is estimated to reach 82 million in 2030 and 152 million in 2050. According to the Alzheimer’s Society, one in four hospital beds in the UK are occupied by a person with dementia, while around 22 percent of these admissions are deemed 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,” said Payam Barnaghi, Professor of Machine Intelligence at CVSSP.

Professor Adrian Hilton, Director of CVSSP, added: “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.”

Source: University of Surrey