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Using topic models to detect behaviour patterns for healthcare monitoring
Healthcare systems worldwide are facing growing demands on their resources due to
an ageing population and increase in prevalence of chronic diseases. Innovative residential
healthcare monitoring systems, using a variety of sensors are being developed
to help address these needs. Interpreting the vast wealth of data generated is key
to fully exploiting the benefits offered by a monitoring system. This thesis presents
the application of topic models, a machine learning algorithm, to detect behaviour
patterns in different types of data produced by a monitoring system. Latent Dirichlet
Allocation was applied to real world activity data with corresponding ground truth
labels of daily routines. The results from an existing dataset and a novel dataset
collected using a custom mobile phone app, demonstrated that the patterns found
are equivalent of routines. Long term monitoring can identify changes that could
indicate an alteration in health status. Dynamic topic models were applied to simulated
long term activity datasets to detect changes in the structure of daily routines.
It was shown that the changes occurring in the simulated data can successfully be
detected. This result suggests potential for dynamic topic models to identify changes
in routines that could aid early diagnosis of chronic diseases. Furthermore, chronic
conditions, such as diabetes and obesity, are related to quality of diet. Current research
findings on the association between eating behaviours, especially snacking,
and the impact on diet quality and health are often conflicting. One problem is the
lack of consistent definitions for different types of eating event. The novel application
of Latent Dirichlet Allocation to three nutrition datasets is described. The
results demonstrated that combinations of food groups representative of eating event
types can be detected. Moreover, labels assigned to these combinations showed good
agreement with alternative methods for labelling eating event types
Activity monitoring using topic models
Activity monitoring is the task of continual observation of a stream of events which necessitates the immediate detection of anomalies based on a short window of data. For many types of categorical data, such as zip codes and phone numbers, thousands of unique attribute values lead to a sparse frequency vector. This vector is then unlikely to be similar to the frequency vector obtained from the training set collected from a longer period of time. In this work, using topic models, we present a method for dimensionality reduction which can detect anomalous windows of categorical data with a low rate of false positives. We apply nonparametric Bayesian topic models to address the variable nature of data, which allows for updating the model parameters during the continual observation to capture gradual changes of the user behavior. Our experiments on several real-life datasets show that our proposed model outperforms state-of-the-art methods for activity monitoring in categorical data with large domains of attribute values