Imperial College London Featured PhD Programmes
University of Liverpool Featured PhD Programmes
University of Reading Featured PhD Programmes

An AI Driven Population Health Study - Improving Medication Verification for Cancer Patients


   Faculty of Health: Medicine, Dentistry & Human Sciences

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Prof Shang-Ming Zhou, Dr Edward Meinert, Mrs Andrea Preston  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

Plymouth United Kingdom Applied Statistics Artificial Intelligence Data Analysis Data Science Epidemiology Health Informatics Machine Learning Mathematical Modelling Medical Statistics Pharmacy

About the Project

Applications are invited for a three-year PhD studentship. The studentship will start on 1 October 2021.

Overview of the Studentship

This fully funded PhD studentship is based at the University of Plymouth’s Centre for Health Technology, a leading international research group focused on the design, development and implementation of health technology innovations in health and care. This vibrant team of researchers work within the intersection of digital health, software engineering, data science, clinical artificial intelligence, and innovation strategy, with a primary aim to create a global hub for technology incubation in the South West of the United Kingdom.

 

Project Description

Medication errors, including those in prescribing, dispensing, or administration of a drug, are the single most preventable cause of patient harm. They have a significant impact on the efficiency of the workflow in pharmacy, raise safety concerns for patients, and result in a financial burden on the healthcare systems. Within cancer treatment, emphasis on reducing the number of medication errors has been an active research area for many years, with understanding that interdisciplinary approaches are vital to assure continuous improvement. Opportunities created by the reduction of transaction times for complex computational processes and use of machine learning to support clinical decision making, create a potential catalyst for the development of tools for reduction in medication errors.

This PhD studentship offers an exciting opportunity of exploring AI and machine learning with large clinical data sets residing within electronic health records to create methods to assure the effective use of systemic anticancer treatment (including traditional cytotoxic chemotherapy, immunotherapy, novel oral therapies etc.) without compromising patient safety. The studentship will require application of interdisciplinary skills to enable cooperation between the research, clinical, industry and patient communities in the development of a novel approach which could enhance clinical outcomes.

Supervision Team

This PhD student will be academically advised by Professor Shang-Ming Zhou and Dr Edward Meinert, research scientists with research interests in applied artificial intelligence and machine learning, and computing science in health and care. The student will also be advised by Mrs Andrea Preston, a Macmillan Divisional Lead Haematology & SW Cancer Commissioning Pharmacist. This supervision team will assure the execution of a world-class PhD embedded into the wider digital health ecosystem at the University of Plymouth.

Eligibility

  • This PhD studentship is offered for UK and international applicants. It is supported for 3 years and includes full Home or International tuition fees, plus a stipend of £15,609 per annum (2021/22 rate).
  • Applicants should have:
  1. A first or upper second-class honours degree, and a relevant Master’s qualification in Computing Science, Data Science, Statistics, Health Informatics, Medical Informatics, Bioinformatics, 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.

International Students

International applicants should meet the English language requirements, please see the details from the University’s website https://www.plymouth.ac.uk/international/how-to-apply/english-language-requirements. IELTS Academic 6.5 or above (or equivalent) with 5.5 in each individual category is commonly required by the University’s Doctoral College.

How to Apply

To apply for this position, please visit: https://www.plymouth.ac.uk/student-life/your-studies/research-degrees/postgraduate-research-studentships and select the studentship you would like to apply for. Please clearly state the name of the studentship that you are applying for on your Personal Statement.

A research proposal is required. Please see: https://www.plymouth.ac.uk/student-life/your-studies/research-degrees/applicants-and-enquirers for a list of supporting documents to upload with your application.

Enquiry

If you wish to discuss this project further informally, please contact Professor Shang-Ming Zhou ([Email Address Removed]), Dr Edward Meinert ([Email Address Removed]), or

Mrs Andrea Preston ([Email Address Removed]).

For more information on the admissions process generally, please contact [Email Address Removed].

Closing Date

The closing date for applications is 30 July 2021. Shortlisted candidates will be invited for interview in August.

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.


Funding Notes

This PhD studentship is offered for UK and international applicants. It is supported for 3 years and includes full Home or International tuition fees, plus a stipend of £15,609 per annum (2021/22 rate).
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.