The Urinary Tract Infections (UTIs) are one of the top reasons for unplanned
hospital admissions in people with dementia, and if detected early, they can be
timely treated. However, the standard UTI diagnosis tests, e.g. urine tests,
will be only taken if the patients are clinically suspected of having UTIs.
This causes a delay in diagnosis and treatment of the conditions and in some
cases like people with dementia, the symptoms can be difficult to observe.
Delay in detection and treatment of dementia is one of the key reasons for
unplanned hospital admissions in people with dementia. To address these issues,
we have developed a technology-assisted monitoring system, which is a Class 1
medical device. The system uses off-the-shelf and low-cost in-home sensory
devices to monitor environmental and physiological data of people with dementia
within their own homes. We have designed a machine learning model to use the
data and provide risk analysis for UTIs. We use a semi-supervised learning
model which leverage the environmental data, i.e. the data collected from the
motion sensors, smart plugs and network-connected body temperature monitoring
devices in the home, to detect patterns that can show the risk of UTIs. Since
the data is noisy and partially labelled, we combine the neural networks and
probabilistic neural networks to train an auto-encoder, which is to extract the
general representation of the data. We will demonstrate our smart home
management by videos/online, and show how our model can pick up the UTI related
patterns.