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Details of Grant 

EPSRC Reference: EP/Y018281/1
Title: Self-learning AI-based digital twins for accelerating clinical care in respiratory emergency admissions (SLAIDER)
Principal Investigator: Liu, Professor L
Other Investigators:
Karatzogianni, Professor A Anjum, Professor A Free, Dr RC
Panneerselvam, Dr J Ng, Professor GA Brightling, Professor C
Tyukin, Professor IY Armstrong, Professor NA
Researcher Co-Investigators:
Project Partners:
Bloc Digital NTT DATA Ltd UK
Department: Informatics
Organisation: University of Leicester
Scheme: Standard Research - NR1
Starts: 02 October 2023 Ends: 01 April 2025 Value (£): 619,667
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
11 Jul 2023 Artificial intelligence innovation to accelerate health research Expert Panel Announced
08 Jun 2023 Artificial intelligence innovation to accelerate health research Sift Panel B Announced
Summary on Grant Application Form

Respiratory disease is the third biggest cause of death in England, causing on average 68,000 deaths per year between 2013 and 2019, with an estimated cost of £9.9 billion per year. This resulted in over 200,000 emergency hospital admissions in 2021-22, with this number continuing to rise. The effect of this is most apparent during winter, when respiratory-related admissions double in number due to 'winter pressures', whilst the health service becomes overloaded and preventable deaths occur.

There is room for improvement and new and emerging technologies should be seriously considered. Digital twins are one such technology that has been used for several years in the engineering field. Digital twinning simulates a physical machine, such as a car, by using algorithms (i.e., mathematical or artificial intelligence models) and data obtained from physical machines. A person using the digital twin can then monitor and improve the car by anticipating problems before they happen. Although digital twins have been applied to healthcare before, their use has been restricted to a narrow scope due to limited data, evaluation of hypothetical scenarios, and the fact they are underpinned by non-changing artificial intelligence models, which are trained once, but cannot adapt to new situations.

Our previous work with digital twins leads us to believe that a self-learning approach would have considerable advantages if applied to respiratory-related admissions. Extending digital twins in this way would mean they are able to a) learn and improve from limited data and feedback from the user; b) consider how patients and their environment change over time; c) identify and correct socio-economic biases ethically; and d) ultimately be personalized to individual patients.

We propose to design self-learning health digital twins which will support human judgement in clinical decision making, by prioritising patients and providing information on the general or specific condition of the patient, and by identifying factors which may lead to respiratory disease or deterioration earlier, thus helping to determine any steps that can be taken to improve the situation.

The acute and varied nature of patients with respiratory disease makes them ideal candidates for health digital twin applications. Hence, this project not only considers how best to co-design clinical decision support tools with patients at the centre of clinical practice, but it breaks new ground, by creating feedback loops and corrective processes against bias, which are required to systematically evaluate, validate, and improve clinical decision support at the necessary speed, and in real-time for patients.

In this project, we will design state-of-the-art responsible AI methods with technical innovation, which facilitates the integration of multi-modal data sources and the development of surrogate models for gaining the necessary insight. A self-learning and self-adaptive health digital twin model will be developed based on the responsible AI methods with a clinical decision support tool to offer services and care at the personalised level. A demonstrator system of our health digital twin will be co-designed to suitably evaluate and validate the dependability of our proposed health digital twin in a clinical setting based on real-world case studies, which will be used to consider our clinical questions with continuous feedback to help improve the underlying models.

Through this unique and timely project led by a multi-disciplinary team, we will break new ground in clinical care and decision making, whilst significantly advancing the case for the development and implementation of self-learning health digital twins in clinical practice.

Key Findings
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Organisation Website: http://www.le.ac.uk