Applying Natural Language Processing to real-world patient data to optimise cancer care

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Nearly 400,000 people are diagnosed with cancer each year, causing more than 167,000 deaths. Incidence and mortality are strongly associated with socioeconomic factors, with around 19,000 extra cancer deaths attributed to deprivation. Decisions on how best to treat cancer patients 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 frail, those with multiple medical problems, and ethnic minorities, are under-represented. This means there are large sections of the population, particularly the deprived (who suffer disproportionately from cancer), where the available evidence might not apply, perpetuating health inequalities. 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. The vision of this project is to learn from every patient treated.Artificial Intelligence approaches using real-world data can be used to understand patterns in cancer diagnosis and treatment, and to provide prospective assessment of the impact of healthcare innovations, but need the data to be structured to enable its processing. Modern Electronic Healthcare Records (EHRs) can collect data in the required format. However, historical data and many current sources of patient information (e.g. out-patient letters, radiology reports) often exist only as free-text medical notes, and therefore needs to be coded, i.e. structured, first. In this project, we will develop and apply Natural Language Processing (NLP) technologies to recover structured data from medical notes. We will then use these data to validate and improve models to predict cancer patients' clinical outcomes, and to see if patients' experience of their cancer treatment agrees with clinical assessments of their outcome and might provide early warning of evolving treatment related toxicity in head and neck cancer. This project is an exciting collaboration between the Division of Cancer Sciences and the Department of Computer Science/Alan Turing Institute, and as such the project student will benefit from close proximity to the clinical teams at The Christie NHS Foundation Trust, the largest single site cancer centre in Europe, and data science expertise at the University.