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

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.

The Centre will nurture 55 new PhD researchers who will be highly sought after in technology companies and application sectors where data and intelligence based systems are being developed and deployed. We expect that our graduates will be nationally in demand for two reasons: firstly, their training occurs in a vibrant and unique environment exposing them to challenging domains and contexts (that provide stretch, ambition and adventure to their projects and capabilities); and, secondly, because of the particular emphasis the Centre will put on people-first approaches. As one of the Google AI leads, Fei-Fei Li, recently put it, "We also want to make technology that makes humans' lives better, our world safer, our lives more productive and better. All this requires a layer of human-level communication and collaboration" [1]. We also expect substantial and attractive opportunities for the CDT's graduates to establish their careers in the Internet Coast region (Swansea Bay City Deal) and Wales. This demand will dovetail well with the lifetime of the Centre and provide momentum for its continuation after the initial EPSRC investment.

With the skills being honed in the Centre, the UK will gain a important competitive advantage which will be a strong talent based-pull, drawing in industrial investment to the UK as the recognition of and demand for human-centred interactions and collaborations with data and intelligence multiplies. Further, those graduates who wish to develop their careers in the academy will be a distinct and needed complement to the likely increased UK community of researchers in AI and big data, bringing both an ability to lead insights and innovation in core computer science (e.g., in HCI or formal methods) allied to talents to shape and challenge their research agenda through a lens that is human-centred and that involves cross-disciplinarity and co-creation.

The PhD training will be the responsibility of a team which includes research leaders in the application of big data and AI in important UK growth sectors - from health and well being to smart manufacturing - that will help the nation achieve a positive and productive economy. Our graduates will tackle impactful challenges during their training and be ready to contribute to nationally important areas from the moment they begin the next steps of their careers. Impact will be further embedded in the training programme with cohorts involved in projects that directly involve communities and stakeholders within our rich innovation ecology in Swansea and the Bay region who will co-create research and participate in deployments, trials and evaluations.

The Centre will also impact by providing evidence of and methods for integrating human-centred approaches within areas of computational science and engineering that have yet to fully exploit their value: for example, while process modelling and verification might seem much removed from the human interface, we will adapt and apply methods from human-computer interaction, one of our Centre's strengths, to develop research questions, prototyping apparatus and evaluations for such specialisms. These valuable new methodologies, embodied in our graduates, will impact on the processes adopted by a wide range of organisations we engage with and who our graduates join.

Finally, as our work is fully focused on putting the human first in big data and intelligent systems contexts, we expect to make a positive contribution to society's understandings of and involvement with these keystone technologies. We hope to reassure, encourage and empower our fellow citizens, and those globally, that in a world of "smart" technology, the most important ingredient is the human experience in all its smartness, glory, despair, joy and even mundanity.

[1] https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai/