PhD Studentship: Mining Routinely Collected Electronic Health Records to Identify Effective Dietetic Factors for Optimal Care in General Practice

Mining Routinely Collected Electronic Health Records to Identify Effective Dietetic Factors for Optimal Care in General Practice

DoS: Shang-Ming Zhou (shangming.zhou@plymouth.ac.uk, tel.: 01752 586513)

2^nd Supervisor: Professor Mary Hickson (mary.hickson@plymouth.ac.uk  , tel.: 01752 587542)

3^rd Supervisor: Dr Avril Collinson (avril.collinson@plymouth.ac.uk, tel.: 01752 588848)

Applications are invited for a three-year PhD studentship. The studentship will start on 1st October 2023 or 01 January 2024 for the right candidate.

Project Description

The typical approach to dietetic work in primary care involves outreach clinics and educational initiatives that are provided by dietitians working in community or acute settings, or by offering dietetic clinics inside GP offices taking referrals from GPs. More recently, dietitians have been able to train to become first contact practitioners; a diagnostic clinician able to assess and manage undifferentiated and undiagnosed presentations. However, the ways in which these practitioners operate has yet to be defined and the role in dietetics is in its infancy and requires evaluation.

The aims of this project are to interrogate multiples electronic health databases and build evidence on dietetic care in general practice.  

This study will enable the definition of what areas of general practice workload a dietitian can support and build evidence of dietary factors that influence disease risk and patterns of risk stratification.

The funded PhD student will use statistics and machine learning techniques with data extracted from the electronic health records to generate evidence on dietetic care.  

Eligibility

Applicants should have

1. A first or upper second-class honours degree in an appropriate subject or a relevant Masters qualification in Health Informatics, Medical Informatics, Bioinformatics, Health Data Science, Computing Science, Statistics, or any areas related;
2. Interest in working with real-world problems and large data sets;
3. Excellent proficiency in English and outstanding communication skills;
4. Strong analytical and programming skills;
5. A “can do”, positive attitude with an aspiration to change the world.

• Experience in machine learning is advantageous.
• Experience in publication of peer-reviewed literature is desirable.

If your first language is not English, you will need to meet the minimum English requirements for the programme, academic IELTS score of 6.5 or above (with no less than 5.5 in each component test area) or equivalent.

Funding

To view the funding details, please visit here.

Funding for: UK Students, International Students

Funding amount:

The studentship is supported for 3 years and includes full Home tuition fees plus a stipend of £17,668 per annum 2022/23 rate (2023/24 rate TBC)

If you wish to discuss this project further informally, please contact Professor Shang-Ming Zhou  via shangming.zhou@plymouth.ac.uk

To apply for this position please click 'Apply' above.

Please clearly state the name of the DoS and the studentship title that you are applying for on the top of your Personal Statement.

Please see here for a list of supporting documents to upload with your application.

• Provision of a research proposal is advantageous.

For more information on the admissions process generally, please visit our How to Apply for a Research Degree webpage or contact the Doctoral College.

The closing date for applications is 12 noon on 31^st July 2023.

Shortlisted candidates will be invited for interview shortly after the deadline. We regret that we may not be able to respond to all applications. 

Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful on this occasion.

Advert information