  Machine learning techniques combined with in-home monitoring technologies
provide a unique opportunity to automate diagnosis and early detection of
adverse health conditions in long-term conditions such as dementia. However,
accessing sufficient labelled training samples and integrating high-quality,
routinely collected data from heterogeneous in-home monitoring technologies are
main obstacles hindered utilising these technologies in real-world medicine.
This work presents a semi-supervised model that can continuously learn from
routinely collected in-home observation and measurement data. We show how our
model can process highly imbalanced and dynamic data to make robust predictions
in analysing the risk of Urinary Tract Infections (UTIs) in dementia. UTIs are
common in older adults and constitute one of the main causes of avoidable
hospital admissions in people with dementia (PwD). Health-related conditions,
such as UTI, have a lower prevalence in individuals, which classifies them as
sporadic cases (i.e. rare or scattered, yet important events). This limits the
access to sufficient training data, without which the supervised learning
models risk becoming overfitted or biased. We introduce a probabilistic
semi-supervised learning framework to address these issues. The proposed method
produces a risk analysis score for UTIs using routinely collected data by
in-home sensing technologies.
