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Details of Grant 

EPSRC Reference: EP/P00993X/1
Title: ARISES: An Adaptive, Real-time, Intelligent System to Enhance Self-care of chronic disease
Principal Investigator: Georgiou, Dr P
Other Investigators:
Toumazou, Professor C Herrero, Dr P Oliver, Professor N
Spence, Professor R
Researcher Co-Investigators:
Project Partners:
Cellnovo Ltd Dexcom Inc ICON Clinical Research (UK) Ltd
Imperial College Healthcare NHS Trust
Department: Electrical and Electronic Engineering
Organisation: Imperial College London
Scheme: Standard Research
Starts: 19 December 2016 Ends: 18 September 2020 Value (£): 1,335,436
EPSRC Research Topic Classifications:
Human-Computer Interactions Information & Knowledge Mgmt
Med.Instrument.Device& Equip. Mobile Computing
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Sep 2016 Intelligent Technologies Prioritisation Panel Announced
Summary on Grant Application Form
This research brings together a multidisciplinary collaborative team of engineers, clinicians and patients to deliver a user driven, patient centric, bespoke technology to treat chronic health-conditions. The proposal will develop an Adaptive, Real-time, Intelligent System (ARISES) that will run on a smart phone locally and collect data from multiple sources to deliver an intervention to the patient that allows self-management of chronic disease. The core of ARISES will use Case-based-reasoning (CBR), a consolidated artificial intelligence technique which can solve problems in much the same way as a human does, using historical data and scenarios as a reference to recommend a current solution which can treat the patient. CBR is also powerful in that it has the capability to be adaptive according to patient lifestyle and behavior and always provide the most optimum solutions for a given set of resources. ARISES will have the capability to collect data from wearable devices such as smart-watches, activity monitors, hear rate monitors and continuous glucose meters and using smart-algorithms will be able to extract meaningful information to provide to the CBR system. Underpinning this will be energy efficient algorithms which always make ARISES aware of what sensors are connected to the patients local area network, safety systems that minimise the risk of any possible undesired event related to the management of the disease, and a data security to make sure information is protected against non-authorised access. ARISES will provide a generic framework which can be used to treat many chronic diseases such as asthma, chronic obstructive airways disease, hypertension, heart failure, ischaemic heart disease, arrhythmias and chronic neurological conditions. Given the global incidence, as an exemplar chronic condition to demonstrate its use we have chosen diabetes which currently affects 3% of the world's population, and we will target improvement in glycaemic control which can reduce micro- and macrovascular complications associated with the disease. In this context the system will promote the self-management of diabetes by optimizing glucose control through insulin dosage recommendations, exercise and physical activity support, carbohydrate recommendations to prevent hypoglycaemia, and behavioral change through education.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.imperial.ac.uk