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

백설경; 박혜진; 강성홍; 최준영; 박종호

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

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머신러닝을 이용한 급성심근경색증 환자의 퇴원 시 사망 중증도 보정 방법 개발에 대한 융복합 연구

Type: Academic journal

Author: 백설경 박혜진 강성홍 최준영 박종호

Journal: The Korea Society of Digital Policy & Management 디지털융복합연구 디지털융복합연구 제17권 제2호 KCI Accredited Journals

Published: 2019.1

Pages: 217 - 230 (14page)

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

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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.

#Severity-adjustment #Acute Myocardial Infarction #Comorbidity #Machine Learning #Convergence study

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