Dementia represents one of the toughest medical and economic challenges facing our society today. Around 850,000 people in the UK have dementia and the number of people affected will continue to grow as the population ages. There are currently no effective therapeutics for any of the neurodegenerative conditions that give rise to dementia. To help people with dementia, we develop a digital platform to make the investigation of the clinical team more efficiently.
In this project, a model is designed to analyse the risk of Urinary Tract Infections (UTIs). UTIs are one of the top reasons for unplanned hospital admissions in people with dementia, and if detected early, they can be treated and avoid unplanned hospital admissions.
To provide ealier detection of UTI symptoms, we deploy several sensors to collect the daily activaties patterns.
We have colletect the physiological data
also the environmental data are collected
To detect the symptoms of UTI, we care more about the environmental data. We have deployed eight environmental sensors in the patients' home to collected thoses data. Here is how the data looks like:
To help our model to learn the data patterns more efficiently, we aggregate the values of each sensor within one hour. Here is the heat maps of a UTI and a no-UTI cases after aggregating.
The collected data will be analysed by our machine learning models and report to the clinical team if an abnormal case is detected. However, this could be really a challenging task. Verifying the cases are positive or not are time-consuming and a majority of our samples are unlabelled. Actually, only around 1.5% of the data is labelled.
To deal with this issue, we leverage the semi-supervised learning techniques to train the models.
Different from the conventional semi-supervised learning models, we leverage the probabilistic neural networks to estimate the density distribution of the data. We leverage the concept of self-training to train the auto-encoder and increase the margin between the positive and negative samples. Please check our paper for the further details.
We have comapred our model with other semi-supervised models, the results show that our model outperform others.
Now, this model is deployed and running to detect the UTI symptoms.