Advanced Machine Learning to Improve Patient Care and Outcome using Real-time Hospitalisation Data

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The overall aim of this project is to develop efficient and effective machine learning and data analysis techniques to analyse a diverse and a large quantity of patient bedside data, in order to improve care provision, patient experience, outcome, and resource management. This involves developing novel techniques that can not only handle highly complex data that are collected from a complex environment but also provide clear reasoning and justification so that clinicians and NHS managers can be fully informed in their decision making and patient care. The successful outcome from this project can play an important role in improving both reactive patient care and forward-looking patient management.The patient bedside data is envisaged to have a great variability from patient to patient, which poses a significant challenge to data processing and building predictive models. The types of data that are collected also covers a wide range, from patient vitals, medications, to care provisions. Sparsity in the dataset introduces additional challenges to both generative and discriminative tasks. Together with domain experts, the project will initially focus on one or two clinical problems, such as sepsis. Severe sepsis and septic shock present a significant healthcare challenge within medicine despite modern advances in antibiotics and acute care. With both a high prevalence and significant mortality rate, sepsis remains the primary cause of death from infections resulting in significant concerns for practitioners. Specifically, within UK statistics, prognosis of a septic patient indicates a 35% mortality rate during ICU stay, 47% mortality rate during hospital spell and a 63% rate of hospital readmission within the first year. Such a severe prognosis is additionally met with a high prevalence rate of 27.1%of adults meeting severe sepsis criteria within the 24 hours of ICU admission. Such statistics provide a snapshot into the significant severity of septic development within a patient. Patients with sepsis take up a significant proportion of hospital beds. The real-time bedside data provides unique opportunities to discover earlier biomarkers or indicators. They also can improve our understanding of prognosis, as well as better resource management between general wards and ICUs. We will build upon our collective expertise in machine learning [1-4], data analysis, human centred computing, and mathematical modelling in order to tackle these technical challenges. For example, on developing novel deep neural network models in order to predict hospitalisation for dementia patients. Our novel neural network models are capable of predicting hospitalisation, six months in advance, using patient health records.This project also has a significant component related to the wider context of employing machine learning in (critical) decision making and human centred computing. A number of considerations.1) The current early warnings trigger interventions. By definition, those patients for whom interventions are triggered are those who are more ill, so disentangling prediction and outcome with the ML is problematic and hence an important research issue. This reflexivity is an issue that occurs in all forms of visual analytics (See European Visual Analytics roadmap [6], so insights here may well be applicable in other domains.2) The issue of empowerment of ward is also very important. It was interesting in that the metrics are often seen as dis-empowering, impositions form management, but here it seems that the transparency they bring is crucial. Maintaining this transparency if, for example, ML techniques are used, will be a critical issue.3) Spatial movement of patients is another of the places where the localised bed data connects into larger contexts. It is reasonable to assume each move carries risks, but also benefits in terms of improved utilisation or having patients in more appropriate hospital wards.