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http://dx.doi.org/10.14400/JDC.2019.17.2.217

Convergence Study in Development of Severity Adjustment Method for Death with Acute Myocardial Infarction Patients using Machine Learning  

Baek, Seol-Kyung (Ajou University Hospital)
Park, Hye-Jin (Dept. of International Healthcare Administration, Daegu Catholic University)
Kang, Sung-Hong (Dept. of Health Policy & Management, Inje University)
Choi, Joon-Young (Dept. of Hospital health information, Cheongam College)
Park, Jong-Ho (Kyeimyoung University Dongsan Medical Center)
Publication Information
Journal of Digital Convergence / v.17, no.2, 2019 , pp. 217-230 More about this Journal
Abstract
This study was conducted to develop a customized severity-adjustment method and to evaluate their validity for acute myocardial infarction(AMI) patients to complement the limitations of the existing severity-adjustment method for comorbidities. For this purpose, the subjects of KCD-7 code I20.0 ~ I20.9, which is the main diagnosis of acute myocardial infarction were extracted using the Korean National Hospital Discharge In-depth Injury survey data from 2006 to 2015. Three tools were used for severity-adjustment method of comorbidities : CCI (charlson comorbidity index), ECI (Elixhauser comorbidity index) and the newly proposed CCS (Clinical Classification Software). The results showed that CCS was the best tool for the severity correction, and that support vector machine model was the most predictable. Therefore, we propose the use of the customized method of severity correction and machine learning techniques from this study for the future research on severity adjustment such as assessment of results of medical service.
Keywords
Severity-adjustment; Acute Myocardial Infarction; Comorbidity; Machine Learning; Convergence study;
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Times Cited By KSCI : 14  (Citation Analysis)
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