  Behavioural symptoms and urinary tract infections (UTI) are among the most
common problems faced by people with dementia. One of the key challenges in the
management of these conditions is early detection and timely intervention in
order to reduce distress and avoid unplanned hospital admissions. Using in-home
sensing technologies and machine learning models for sensor data integration
and analysis provides opportunities to detect and predict clinically
significant events and changes in health status. We have developed an
integrated platform to collect in-home sensor data and performed an
observational study to apply machine learning models for agitation and UTI risk
analysis. We collected a large dataset from 88 participants with a mean age of
82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a new
deep learning model that utilises attention and rational mechanism. The
proposed solution can process a large volume of data over a period of time and
extract significant patterns in a time-series data (i.e. attention) and use the
extracted features and patterns to train risk analysis models (i.e. rational).
The proposed model can explain the predictions by indicating which time-steps
and features are used in a long series of time-series data. The model provides
a recall of 91\% and precision of 83\% in detecting the risk of agitation and
UTIs. This model can be used for early detection of conditions such as UTIs and
managing of neuropsychiatric symptoms such as agitation in association with
initial treatment and early intervention approaches. In our study we have
developed a set of clinical pathways for early interventions using the alerts
generated by the proposed model and a clinical monitoring team has been set up
to use the platform and respond to the alerts according to the created
intervention plans.
