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Causal AI using EHR data for clinical decision support (4-year doctoral studentship, Sep 22 start)


   School of Physical and Chemical Sciences

  , Dr Alison O'Neil, Prof Maria Liakata  Applications accepted all year round  Funded PhD Project (European/UK Students Only)

About the Project

The following doctoral studentship is available at Queen Mary University of London in partnership with Canon Medical Research Europe (https://research.eu.medical.canon) and will start in September 2022. 

For more information on how to apply for this opportunity or other funded opportunities available please visit our website (https://www.qmul.ac.uk/dce/applications). We are currently accepting applications on a rolling basis (first come, first served - until the position is filled). Please aim to submit your application as soon as possible.

The funding will cover tuition fees at the Home student rate (please see below for information), and an enhanced maintenance stipend (£21,245 tax free, including LWA in 2022/2023) for the duration of the programme, subject to satisfactory progress. The programme duration is a maximum of 48 months full-time (part-time options may be available).

The successful candidate will be enrolled as a full-time student on the Doctorate in Data-Centric Engineering at Queen Mary University of London. The Engineering Doctorate (EngD) is a doctoral level degree (equivalent to a PhD) where the student pursues a research project while based within a company. In this case, the successful candidate will have the opportunity to be based at Canon Medical Research in Edinburgh for the duration of the studentship, with regular visits to Queen Mary University. If the candidate prefers to be mainly based elsewhere, it will be possible to negotiate a combination of remote work with regular visits to Canon Medical Research in Edinburgh and Queen Mary University of London. The Engineering Doctorate offers a unique opportunity to get professional experience while gaining a doctoral qualification.

Project title:

Causal AI using EHR data for clinical decision support

Project abstract:

The digitisation of electronic health records (EHRs) has opened up opportunities to analyse and leverage data at scale for the purpose of supporting health professionals to deliver effective and efficient care. In particular there is high interest in targeting resources for best patient outcomes. To achieve this, we need algorithms that can handle large amounts of heterogeneous data. For instance the format of EHR data is heterogeneous: both structured (e.g. clinical codes, prescriptions) and unstructured (e.g. free-text letters), at different levels of detail (e.g. ward notes vs discharge summaries), for different purposes (referral, diagnosis, billing, etc.), and by different sets of actors (doctors, nurses, physiotherapists, etc.). Further, EHR data is heterogeneous in terms of topic, describing different aspects of a patient’s care (e.g. medications, diagnoses, treatments, social factors), for different medical specialties (e.g. cardiology, psychiatry, dermatology) and in different settings (e.g. primary care vs secondary care). Algorithms require understanding of the interplay between factors, including the causal relationships, in order to model the data in a way that enables intervention at an individual or system level.

This project will leverage large EHR datasets available in the public domain and via Canon clinical collaborators, build machine learning models for characterising and stratifying patients. The focus will be on two lines of investigation:

  • developing general medical machine learning methods that can be trained using supervision from existing data, with minimal additional expert input e.g. by cross-referencing different documents and data types, or by using clinical coding as supervision to extract further structure from free-text data.
  • exploring methods of integrating understanding of causality such that machine learning models can provide accurate explanations of patient status and predictions for future outcomes given potential interventions

Supervisors: Dr Alison O'Neil (Principal Scientist, Canon Medical Research) and Prof Maria Liakata (QMUL)

Eligibility: 

Candidates should usually have:

-         an undergraduate degree of 2:1 or above (or equivalent non-UK qualification) in a relevant subject such as Engineering, Physics, or Computer Science. Candidates without a first degree or qualifications below a 2:1 degree are welcome to apply and will be considered on a case by case basis should they have at least five years of relevant work experience (post qualification).

-         been awarded their most recent degree (if any) before November 2019. There is potential flexibility for more recent graduates, please contact Prof. Eram Rizvi for more details ().  

-         at least two years' full-time employment (or equivalent) in a related field prior to applying, preferably for a UK company or a company with a substantial connection to the UK;

We particularly welcome applicants who:

-         Who have faced barriers to accessing education, e.g. financial barriers, perceived lack of alignment between education and professional development needs, lack of flexibility around work and/or family commitments;

-         Have taken a career break;

-         Belong to or identify with groups who have been historically underrepresented and marginalised in Science, Engineering, and Technology, as well as in doctoral-level study

-         Have not considered pursuing a Doctorate before

Funding eligibility criteria

We appreciate all talent and skills, and we welcome applicants of all backgrounds and identities. Our applications are open to both home and international students who meet all the programme entry criteria. We can allocate up to 30% of our places to International Students. Regretfully however, the funding allocated to us does not cover the full cost of tuition for those classified as international students. If a candidate classified for fees at the international student rate is offered a place on the programme, the difference between home and international fees will have to be covered from other sources, e.g. additional funding, the employer, or the student directly.

 In order to be classified as a Home Student and be eligible for full funding, applicants must:

-         Have no restrictions on how long they can stay in the UK 

-         have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For example: 

o  UK nationals

o  EU nationals seeking permanent settlement in the UK

o  nationals of any other country with unrestricted UK immigration status (e.g. indefinite leave to remain) and full-time residence in the UK.

For more information, visit our website: www.qmul.ac.uk/dce/applications/#Eligibility

If in doubt please contact us with full details of your degree award dates, nationality, immigration status, and residency since 2019.


Funding Notes

Our applications are open to both home and international students who meet all the programme entry criteria (see: View Website).
We can allocate up to 30% of our places to International Students. Regretfully however, the funding allocated to us does not cover the full cost of tuition for those classified as international students. If a candidate classified for fees at the international student rate is offered a place on the programme, the difference between home and international fees will have to be covered from other sources, e.g. additional funding, the employer, or the student directly.

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