Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.
Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.
School of Business Information Technology and Logistics, RMIT University, Melbourne, Australia.
Fatemeh Hoda
FH
Moghimi
Michael
M
Cheung
Nilmini
N
Wickramasinghe
D001315:Australia; D003199:Computer Systems; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D057286:Electronic Health Records; D055991:Health Records, Personal; D006330:Heart Defects, Congenital / Q000175:diagnosis* / Q000601:surgery*; D006801:Humans; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods*