Healthcare professionals, especially surgeons must make complex decisions with far reaching consequences and associated risks. As has been shown in other industries, the ability to drill down into pertinent data to explore knowledge behind the data greatly facilitates superior, informed decisions to ensue. This proposal proffers an Intelligent Risk Detection (IRD) Model using data mining techniques followed by Knowledge Discovery in order to detect the dominant risk factors across a complex surgical decision making process and thereby to predict the surgery results and hence support superior decision making. To illustrate the benefits of this model, the case of the Congenital Heart Disease (CHD) is presented.
Knowledge Discovery, Data Mining, Risk detection, Decision making, Congenital Heart Disease (CHD)
ISBN: [978-1-86435-644-1]; Doctoral consortium paper
Moghimi, Fatemeh Hoda; Zadeh, Hossein Seif; and Wickramasinghe, Nilmini, "An Intelligent Risk Detection Framework Using Knowledge Discovery To Improve Decision Efficiency In Healthcare Contexts: The Case Of Paediatric Congenital Heart Disease " (2011). PACIS 2011 Proceedings. 136.