Issariya UbolthamNakornthip PrompoonWirichada Pan-NgumChulalongkorn UniversityMahidol University2018-12-112019-03-142018-12-112019-03-142016-08-232016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings. (2016)2-s2.0-84988019713https://repository.li.mahidol.ac.th/handle/20.500.14594/43429© 2016 IEEE. 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.Mahidol UniversityComputer ScienceEnergyMathematicsConference PaperSCOPUS10.1109/ICIS.2016.7550749