Exploring Patient Focused and Machine Learning interventions for Rare Disease Diagnosis

In this project, the theme of a patient held data will be leveraged alongside AI driven approaches to track the journeys of various individuals for the purpose of suggesting potential rare diseases. Typically, work in this area has focused on curating data held by healthcare providers (e.g., Hu 2016), but in the current structure of UK based IT systems this would be difficult to achieve due to poor integration of data streams.Instead, this project will explore opportunities to empower patients to have a greater role in the diagnosis of their diseases. Within rare disease groups, patients often become `expert partners' (Aymé 2008) after diagnosis, however while on the journey to diagnosis patients are lacking in fundamental knowledge to be able to contribute todiscussions about potential conditions. By placing an intervention in the hands of patients, there is potential for earlier conversations to be had about potential conditions. One such approach trailed in previous works has seen the use of a 'healthcare passport' utilised as a way to inform a multitude of stakeholders in an individuals treatment of the status of their health and scheduled appointments [Leavey 2016].By following a participatory design process (Bowen 2010), this project will design a digital based health journey passport intervention to track an individual's engagements with healthcare provides over time. The outcome of this process will inform both:
the key requirements for a digital intervention that will be used to capture patient health engagements-a knowledge base of the experiences of patients already diagnosed with rare conditions and the pathways that they engaged with
This knowledge base will then form a query set that will be submitted to a portal such as the SAIL Databank project in order to produce a data set that can be used to analyse the pathways of a greater number of rare disease patients. By then adopting methods of machine learning and data analytics (e.g., Perer 2013), similar journey patterns amongst other diagnosed patients will be compared to and potential diseases will be outlined to a patient, in partnership with patient entered symptoms. The intervention will then provide a candidate list of rare disease and enable the patient to highlight potential rare diseases that might explain a condition to a healthcare provider. The nature of rare diseases typically means that many healthcare providers are unfamiliar with either the disease itself, or the symptoms that are typical of a condition (Vandeborne 2016).Following on from the creation of a patient digital tool, and a system to analyse journeys has been developed, the system will be tested in the wild with target members identified by Amicus and other project partners. A key aim of this evaluation will be formed around the experiences of patients engaging in this practise, and also to evaluate more quantitively the data driven approach.

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/