Extracting scientific insights from online health boards using artificial intelligence
- Supervisors: Prof Conrad Bessant (primary) and Dr Fabrizio Smeraldi (EECS, secondary)
- Funding: CONACyT
The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres, according to the 2014 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 150 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of high performance computing faciltiies supported by experienced staff, as well as a range of student support services.
Supervisors Bessant and Smeraldi have a strong track record applying their expertise in artificial intelligence (AI) to answer questions in biomedicine, with a recent focus on mining clinical insigths from online health boards. This studentship will be based at the Digital Environment Research Institute, QMUL's dedicated centre for applied AI research, housed in a modern research space in London's vibrant Whitechapel neighbourhood.
Training and development
Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.
What impact does a disease have on the daily lives of patients? How does a physical illness affect someone’s mental health? What anxieties do patients have about participating in a clinical trial? Answering questions like these is crucial to the development of new drugs, diagnostic methods and healthcare practices, but eliciting such insights from patients is traditionally a slow and costly process involving recruitment of patients to focus groups and structured interviews.
To get more timely and extensive patient insights we have developed a software solution that combines AI and natural language processing with manual annotation to mine clinical insights from conversations occurring on Online Health Boards (OHBs). OHBs are dedicated internet forums where millions of patients worldwide have been discussing their experience, anxieties and needs for over 20 years. Using our technology, we have already delivered insights into mental health effects of pandemic lockdown , how patients interact online  and lifestyle impacts of chronic heart failure .
We are now seeking an outstanding graduate with a passion for devising novel computational solutions to data-rich scientific questions, to help us further develop and apply novel algorithms to capture the lived experience of patients from the millions of free text posts available on OHBs. Methodologies to be applied during this project will include natural language processing, deep learning, and network science.
This studentship is open to students applying for CONACyT funding. CONACyT will provide a contribution towards your tuition fees each year and Queen Mary will waive the remaining fee. CONACyT will pay a stipend towards living costs to its scholars.
Eligibility and applying
Applicants are required to provide evidence of their English language ability. Please see our English language requirements page for details.
Applicants will need to complete an online application form by this date to be considered, including a CV, personal statement and qualifications. Shortlisted applicants will be invited for a formal interview by the project supervisor. Those who are successful in their application for our PhD programme will be issued with an offer letter which is conditional on securing a CONACyT scholarship (as well as any academic conditions still required to meet our entry requirements).
Once applicants have obtained their offer letter from Queen Mary they should then apply to CONACyT for the scholarship as per their requirements and deadlines, with the support of the project supervisor.
Only applicants who are successful in their application to CONACyT can be issued an unconditional offer and enrol on our PhD programme.
1. Analysis of mental and physical disorders associated with COVID-19 in online health forums: a natural language processing study. BMJ Open, November 2021.
2. Visualising the Topological Structure of Health-related Message Board User Networks. APPIS, 2018.
3. Rapid concept elicitation by AI-assisted coding of online patient conversations, DIA Digital Technology for Clinical Trials, 2021.