Application of Machine Learning to Personalised Medicine in Children

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As electronic health records progress to capture a wider and more complete range of data, such as continuous monitoring signals, so too does the ability of computational methods to analyse patients and uncover deeper reaching commonalities and differences.This project will research machine learning methods that extend beyond the analysis of traditional patient features, such as prior diagnoses, weight, sex, or their history of known risk-factors. Great Ormond Street Hospital - and indeed the broader medical community - has been integrating what were once distributed and informal data storage system into highly structured and centralised databases. These have obvious security benefits, but importantly allow efficient access to data for research and for the deployment of assistive tools. These modernised Electronic Health Records (EHRs) extend beyond disease and treatment history, including information as granular as operating theatres, presiding surgeons, and - of note - records of inpatient signals such as heart rate and oximetry. Such a cohesive picture of each patient, in tandem with the entire history of patient outcomes contained in the HER, permits the identification of fine-grained patient cohorts, in contrast to the broad cohorts in gold-standard randomised controlled trials (RCTs). This project will research the use of characteristic features representing a patient's full longitudinal history, including diagnoses, interventions and treatments. This will allow for a more comprehensive computational model of a patient and their needs.Having created these patient models, this project will further research how these novel patient representations can be used with machine learning methods and longitudinal GOSH EPR data to (1) find latent commonalities and differences in groups of patients, that can support patient-level predictions and associate with clinical outcomes, and (2) analyse, understand and suggest modifications to existing treatment pathways. A successful outcome would be the internal deployment and clinical validation of an assistive tool using these methods.This project aligns with two EPSRC general research themes, namely "Engineering" and "Healthcare technologies" and also falls within the remit of the strategic research area of Artificial Intelligence. The research will also tap into EPSRC named strategic priorities "Frontiers in Engineering and Technology" and "Transforming Health and Healthcare".