2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (2016)
June 26, 2016 to June 29, 2016
Issariya Uboltham , Department of Computer Engineering Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
Nakornthip Prompoon , Department of Computer Engineering Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
Wirichada Pan-ngum , Mahidol-Oxford Tropical Medicine Research Unit (mORU) Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
Acute Kidney Injury (AKI) is common and harmful disorder in hospitalized patients. It is associated with poor outcomes such as a decrease chance of survival, longer hospital stays and an increase progression of chronic kidney disease. To diagnosis AKI, the KDIGO clinical practice guideline has been published for providing standardized criteria of AKI definition and the recommendation in medical pathway. Moreover, early detection of AKI in patient at risk can also improve the outcomes. This paper presents an approach to assist the doctor in diagnosis and decision making process. First, the risk factors of AKI were identified using data mining approach based on Decision Tree classification technique. Simple Cart and J48 were selected as the algorithms for this process. Second, a concept of tool requirements and design named “AKIHelper” is presented. This tool is created based on KDIGO guideline which is expected to use for diagnosis and staging severity of AKI.
Diseases, Data mining, Guidelines, Kidney, Hospitals, Medical diagnostic imaging
I. Uboltham, N. Prompoon and W. Pan-ngum, "AKIHelper: Acute kidney injury diagnostic tool using KDIGO guideline approach," 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan, 2016, pp. 1-6.