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Knowledge capture for self management of long-term conditions

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

McCullagh, PJ, Nugent, CD, Zheng, H, Zhang, Shumeii, Huang, Y, Davies, Richard, Black, Norman, Wright, Peter, Hawley, Mark, Eccleston, Chris, Mawson, Sue and Mountain, Gail (2011) Knowledge capture for self management of long-term conditions. In: International Congress on Telehealth and Telecare, London, UK, 1–3 March 2011, London. Igitur publishing. 2 pp. [Conference contribution]

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URL: http://www.ijic.org

Abstract

Introduction: Self-management encourages a person with a long-term condition (LTC) to solve problems, take decisions, locate and useresources and take actions to manage their condition.Aims and objectives: The aim of this paper is to discover appropriate knowledge to facilitate the self-management paradigm. For use ina computing platform, such knowledge must be expressed in digital form in a database.Methods: The SMART2 [1] project is developing a Personalised Self Management System (PSMS) for use in the home environment andin the immediate community for people living with the LTCs: stroke, chronic pain and congestive heart failure (CHF). This system relieson access to clinically validated digital media for therapeutic instruction and appropriate feedback, based on current use.Results: Two approaches to knowledge acquisition were used: (i) obtaining knowledge from the stakeholders, using a user-centred designapproach (ii) obtaining knowledge from the PSMS, as the user undertakes activities of daily living in pursuit of their end-goal. We haveutilized data mining and classification techniques to quantify PSMS interventions.Conclusions: Knowledge capture requires abstraction of key process used by the stakeholders and the use of data mining procedures toobtain information patterns, which can be used to promote self-management.

Item Type:Conference contribution (Poster)
Keywords:self management, chronic pain, stroke, coronary hearth failure, decision support
Faculties and Schools:Faculty of Art, Design and the Built Environment
Faculty of Computing & Engineering
Faculty of Art, Design and the Built Environment > School of the Built Environment
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Built Environment Research Institute
Computer Science Research Institute
Built Environment Research Institute > Centre for Sustainable Technologies (CST)
Computer Science Research Institute > Smart Environments
ID Code:20060
Deposited By:Dr Paul McCullagh
Deposited On:04 Nov 2011 12:16
Last Modified:04 Nov 2011 12:16

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