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Mining, knowledge and decision support.

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

Finlay, D, Nugent, CD, Wang, Haiying, Donnelly, Mark and McCullagh, PJ (2010) Mining, knowledge and decision support. Technology and health care : official journal of the European Society for Engineering and Medicine, 18 (6). pp. 429-41. [Journal article]

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Abstract

Decision support systems (DSS) are software entities that assist the physician in the decision making process. They have found application in medicine due to the large amounts of data (e.g. laboratory measurements such as blood pressure, heart rate, body-mass index) and information (e.g. patient history, population statistics based on age and sex) that must be considered before diagnosing any disease or recommending a therapy. A well known example is the embedded software in defibrillators which allows a 'shock' to be delivered, by analyzing the electrocardiogram for known conditions (heart attack). The shock can restart the heart and timely delivery can resuscitate the patient. As well as assisting in primary diagnosis, a DSS can reduce medical error, assist compliance with clinical guidelines, improve efficiency of care delivery and improve quality of care. Decision support still has significant acceptance issues in clinical routine, but can achieve more prominence, as systems are demonstrated to provide effective knowledge based support. Data mining is often used to provide some insight to a data set and update our accepted knowledge. In this section, we discuss a study which examines where electrocardiographic information should be recorded from a patient's torso in order to increase diagnostic yield.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:17194
Deposited By:Dr Dewar Finlay
Deposited On:07 Mar 2011 16:21
Last Modified:08 Apr 2014 16:05

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