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Details of Grant 

EPSRC Reference: EP/N027280/1
Title: Healtex: UK Healthcare Text Analytics Research Network
Principal Investigator: Nenadic, Professor G
Other Investigators:
Stewart, Professor R Gaizauskas, Professor R Robertson, Professor D
Spasic, Professor I
Researcher Co-Investigators:
Project Partners:
Arthritis Research UK (ARUK) HealthUnlocked South London and Maudsley NHS Trust
UK Renal Registry
Department: Computer Science
Organisation: University of Manchester, The
Scheme: Standard Research - NR1
Starts: 01 June 2016 Ends: 31 May 2019 Value (£): 340,421
EPSRC Research Topic Classifications:
Artificial Intelligence Computational Linguistics
Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
26 Jan 2016 HT Networks Plus Panel Announced
23 Feb 2016 HT NetworksPlus Interviews Announced
Summary on Grant Application Form
Healthcare is a prime example of "big data science" with a number of challenges and successful stories where actionable information extracted from data has improved and saved lives [1]. The majority of concerted efforts focused on real-time processing and integration of structured data streams coming from clinical coding, diagnostic tests, sensor measurements, questionnaires, etc. to support timely clinical interventions and facilitate patients' self-management. Nonetheless, natural language remains the main means of communication within healthcare with its written accounts becoming increasingly available in an electronic form, thus giving rise to big text data. Prominent examples include text data embedded within electronic health records (e.g. referral letters, case notes, pathology reports, hospital discharge summaries, etc.), patient-reported outcome measures (e.g. questionnaires, diaries, etc.) or unsolicited informal feedback shared openly on the Web 2.0 (e.g. social media, fora, etc.). Unfortunately, the capacity to effectively utilise information from unstructured text data on a big scale is lagging behind its structured counterpart. The fact that the majority of actionable information in healthcare is contained within text data (some estimates shows as much as 85%) clearly indicates a potential to dramatically transform community health and care by the ability to process and integrate such information in real time. However, automated and large-scale "understanding" of diverse healthcare sublanguages is still largely unsolved research challenge due to their dynamics, idiosyncrasy, ambiguity and variability.

The aim of this proposal is to build a UK-wide multi-disciplinary research network in order to explore the barriers to effectively utilising healthcare narrative text data, road-map research efforts and principles for sharing text data and text analytics methods between academia, NHS and industry. The network will directly address the "Transforming Community Health and Care" grand challenge by enabling research that will deploy healthcare narratives as real-time sensors and integrate them with the structured data streams into a patient-focused collaborative ecosystem, which will involve healthcare professionals, patients, carers and researchers. Such systemic network of healthcare activities will facilitate informed decision making, timely interventions, deeper digital phenotyping for clinical epidemiology and population-based modelling. On the other hand, by processing patient-generated narratives, which are often a preferred and likely means to provide patient responses (e.g. text messages) to complement structured healthcare data (e.g. signals from wearable devices), we will "use real-time information to support self-management of health and wellbeing".

The main outcome of the network will be a strong, sustainable community that will continue its mission after the initial 3 years of support. Other outcomes will include (1) reports describing the state-of-the-art and challenges for key barriers in harnessing text narratives and making sense from them; (2) a research roadmap for healthcare text analytics; (3) an enlarged membership and expanded collaborations within the network, in particular with early career researchers and internationally; (4) a series of focused pilot/feasibility projects that will inform further developments and kick-start collaborative projects; (5) a collection of research papers at conferences and journals, improving the UK competitiveness in this growing area; (6) several project proposals scoped during the project and prepared for submission; (7) proposals for discipline-bridging personal fellowships, and (8) an interactive registry of healthcare text analytics expertise, resources and tools so that the users and collaborators can identify existing resources and initiate new collaboration.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Organisation Website: http://www.man.ac.uk