Author/Editor		Kokol, Peter; Zorman, Milan; Eich, Hans-Peter; Ohmann, Christian
Title		The difficulties of decision trees in the diagnostic of acute abdominal pain
Type		članek
Source		In: Farres-Sebaaly M, editor. The international NAISO congress on information science innovations (ISI2001); 2001 Mar 17-21; Dubai. Wetaskiwin: ICSC academic press,
Publication year		2001
Volume		str. 1-6
Language		eng
Abstract		Decision support systems that help physicians are becoming a very important part of medical decision making. One of the most viable among them are decision trees, already successfully used for many medical decision making purposes. Transparent and straightforward representation of accumulated knowledge and fast algorithms made decision trees what they are today: one of the most often used symbolic machine learning approaches. This paper describes a new decision tree approach MtDeciT to the clinical filed of acute abdominal pain. Acute abdominal pain manifests itself in considferable diagnostic error rates and negative outcomes, despite improvements by using imaging technology (e.g. ultrasound) and special laboratory investigations. It is a frequent problem with the necessity for urgent management decision. Studies in this area have reported diagnostic accuracy of 60% by the initial examiner and of 80% by the final examiner. Different standard induction techniques (e.g. ID3, NewID, C4.5) were tested on the same prospective database with an overall accuracy in a range between 40% and 48% on the text set.
Descriptors		ABDOMINAL PAIN DECISION MAKING, COMPUTER-ASSISTED DECISION TREES DIAGNOSTIC ERRORS