Machine learning to identify clusters of patients with immune mediated inflammatory illnesses.

Are you passionate about AI’s potential to solve real-world challenges? Join the Healthcare Ecosystems theme of the AI for Collective Intelligence Hub as a PhD student and contribute to cutting-edge research aimed at using large-scale health databases to improve clinical decision making.

Overview of the Research:

This fully-funded 3-year PhD project will use clustering techniques to understand longitudinal multimorbidity patterns in patients with immune-mediated inflammatory diseases (IMIDs). IMIDs are a group of long-term health conditions where inflammation caused by imbalances in the immune system damages the body. IMIDs include rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. They are associated with higher risk of other health conditions (known as comorbidity or multimorbidity), reduced quality of life, and premature death.

Existing treatments for IMIDs do not work in all patients and carry risks. Monitoring for complications of IMIDs is an important part of care. This can be time consuming for patients and their doctors and sometimes requires lots of medical tests, increasing costs for the NHS. While we know the common complications and comorbidities in IMIDs, we also know that some patients are more at risk than others. If we were able to identify patients at highest risk of multimorbidity and predict how this risk changes over time, we could target timely monitoring and institute preventative treatments earlier. Ultimately this will improve patients’ health, reduce unnecessary testing, and alleviate pressures on NHS services.

In this project, you will apply clustering techniques to large scale electronic health records to

·        Discover common patterns in multimorbidity in a cohort of IMID patients and quantify their relative prevalence.

·        Investigate whether data-driven classifications match pre-existing IMID subgroups.

·        Model changes in multimorbidity over time (i.e., longitudinally) within clusters.

You will master the current tools for clustering and develop novel longitudinal methodologies.

You will work alongside a growing network of PhD students and postdocs in health data science at Bath and benefit from a wide range of development and networking activities across the Hub. This is an excellent opportunity to develop your analytics skills in a high-impact project, preparing you for a future career in health research in academia or industry.

Project keywords: clustering, machine learning, electronic health records, multimorbidity, inflammatory diseases

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent) in a relevant subject such as health data science, epidemiology, statistics, computer science or clinical training. A master’s level qualification and/or relevant clinical experience would also be advantageous.  Experience with data analysis (using a language such as R, Python or Stata), ideally through independent project work, is essential.

Enquiries and Applications:

Informal enquiries are encouraged and should be directed to Dr Theresa Smith [email protected]

Formal applications should be submitted via the University of Bath’s online application form for a PhD in Statistics prior to the closing date of this advert.

IMPORTANT:

When completing the application form:

1.      In the Funding your studies section, select ‘University of Bath LURS’ as the studentship for which you are applying.

2.      In the Your PhD project section, quote the project title of this project and Theresa Smith as the lead supervisor. 

Failure to complete these two steps will cause delays in processing your application and may cause you to miss the deadline.

More information about applying for a PhD at Bath may be found on our website.

PLEASE BE AWARE: Applications for this project may close earlier than the advertised deadline if a suitable candidate is found. We therefore recommend that you contact the lead supervisor prior to applying and submit your formal application as early as possible.

Equality, Diversity and Inclusion:

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.