AbstractThe past two decades have seen an unprecedented increase in the amount of data collected on a daily basis. While consumer-orientated internet services, such as Google and Twitter, take up a large proportion of the former, advances in mobile technology and diagnostic tools have allowed biomedical applications to reach a similar scale in variety, velocity and volume. In the past, what used to be sparse biomedical data is now complemented with feature-rich, time-dependent information. As a result, the field is forced to consider novel approaches for extracting actionable information from time-series data sets, guaranteeing reliability and scalability of associated services as well as ensuring a high-degree of compliance from data sources.
This thesis focuses on providing an in-depth evaluation of time-series analyses, scalable IT infrastructure and strategies for improved user engagement. In order to reflect the breadth and broadness of biomedical applications, the topics were distilled into two distinct studies in the fields of remote symptom detection, and large scale patient monitoring.
|Date of Award
|Richard Dobson (Supervisor) & Stephen Newhouse (Supervisor)