About the Project
Decisions on patient treatment are based on evidence which is usually generated through the use of clinical trials. However, only a small fraction of patients participate in these studies, and many patient groups such as the elderly, those with multiple medical problems, and ethnic minorities, are under-represented. This means there are large sections of the population where the available evidence might not apply. Routine ‘real-world’ patient data, collected about every patient as part of their normal treatment, offers an opportunity to provide evidence where clinical trial data doesn’t or will not exist. Artificial Intelligence and machine learning approaches can be used to generate evidence from real-world data but need the data to be structured to enable its processing. Similarly, prospective assessment of the impact of healthcare innovations requires patient and doctor reported clinical outcomes to be amenable to digital statistical analysis. The vision is to learn from every patient treated. Modern Electronic Healthcare Records (EHRs) can collect data in the required format. However, historical data often exists only as free-text medical notes. Furthermore, different medical groups are at different stages in the adoption of structured EHRs and can prioritise the collection of different data items. In this project, we will develop and apply Natural Language Processing (NLP) technologies to recover structured data from medical notes. We will use these data to validate and improve models to predict cancer patients’ clinical outcome, and to see if patients’ experience of their cancer treatment agrees with clinical assessments of their outcome.This project is an exciting collaboration between the Manchester Cancer Research Centre and Department of Computer Science/Alan Turing Institute, and as such will benefit from close proximity to the clinical teams at The Christie NHS Foundation Trust, the largest single site cancer centre in Europe.
Applications are invited from UK nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
To be considered for this project you MUST submit a formal online application form. For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/)
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/
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