title: Mining, knowledge and decision support.
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.
authors.affiliation: School of Computing and Mathematics, University of Ulster, Shore Road, Newtownabbey, Co. Antrim, Northern Ireland, UK. d.finlay@ulster.ac.uk
authors.forename: Dewar D
authors.initials: DD
authors.lastname: Finlay
authors.forename: Chris D
authors.initials: CD
authors.lastname: Nugent
authors.forename: Haiying
authors.initials: H
authors.lastname: Wang
authors.forename: Mark P
authors.initials: MP
authors.lastname: Donnelly
authors.forename: Paul J
authors.initials: PJ
authors.lastname: McCullagh
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D017598:Efficiency, Organizational; D019983:Guideline Adherence / Q000458:organization & administration; D006801:Humans; D019359:Knowledge; D019300:Medical Errors / Q000517:prevention & control; D016571:Neural Networks, Computer; D017410:Practice Guidelines as Topic; D011787:Quality of Health Care / Q000458:organization & administration