Ulster University Logo

Ulster Institutional Repository

III.3. Mining, knowledge and decision support

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

Finlay, Dewar, Nugent, CD, Wang, HY, Donnelly, MP and McCullagh, PJ (2010) III.3. Mining, knowledge and decision support. In: Stud Health Technol Inform. 2010;152:158-71. IOS Press, pp. 158-171. ISBN 978-1-60750-526-6 [Book section]

Full text not available from this repository.

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:Book section
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:17412
Deposited By:Dr Paul McCullagh
Deposited On:12 Apr 2011 16:46
Last Modified:27 Jun 2011 11:11

Repository Staff Only: item control page