Digital health technologies hold promise for transforming healthcare by expanding the ways in which health data can be used. From individual patient management to large-scale organisational planning, an evidence-based approach based on real world data-driven insights is seen as the way to optimise the delivery of healthcare. Artificial intelligence technologies are seen as vital to this endeavour through the development of machine-based algorithms to learn from noisy real-world data and identify patterns and associations that might reveal novel opportunities for health or service improvement. For example, in 2019, 267,449 people were treated for substance use disorders (SUD) in England. Evidence-based psychosocial and behavioural interventions are required to meet the treatment needs of this population. Lack of resources for SUD services means that patients are increasingly being offered online interventions which opens the possibility for enhanced digital experiences through more sophisticated use of their data.
In this project, we will explore fully anonymized data from patients receiving digitally delivered SUD treatment to identify risk factors associated with SUD and the effectiveness of the digital treatment regime. This will be conducted in collaboration with Breaking Free Online (BFO) – a digital health and behavioural science organisation – which provides digital cognitive-behavioural therapy for SUD at scale. The BFO platform have supported approximately 38,000 patients via commissioning treatment providers since 2010. BFO is delivered across approximately 300 treatment services, including the UKs largest providers, e.g. NHS and ‘Change, Grow, Live’. Since 2019, BFO has been delivered in North America – increasing this international footprint is now a major priority. Outputs from this research will be evaluated by BFO for real-world inclusion in the BFO Virtual Care Platform providing a unique opportunity to directly influence real world care and support for SUD individuals with your research.
The candidate should have a strong interest in developing a career in artificial intelligence research and possess a strong quantitative background obtained from a first degree in mathematics, physics, engineering or computer science. The candidate will be required to train and acquire skills in advanced statistical programming. It is expected that the candidate will develop research outputs that will be publishable in the internationally leading scientific journals as well as machine learning conferences such as NeurIPS and ICML. Interdisciplinary collaborations are also embedded within the PhD study period to give the candidate exposure to substance misuse, behavioural science and mental health practice.
Yau Group: http://cwcyau.github.io
Breaking Free Group: https://www.breakingfreegroup.com/
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or overseas equivalent) in a relevant subject.
Applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible.
To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the MRC Doctoral Training Partnership (DTP) website www.manchester.ac.uk/mrcdtpstudentships
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