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http://dx.doi.org/10.5762/KAIS.2018.19.11.126

A study on the development of severity-adjusted mortality prediction model for discharged patient with acute stroke using machine learning  

Baek, Seol-Kyung (Ajou University Medical Center)
Park, Jong-Ho (Keimyung University Dongsan Medical Center)
Kang, Sung-Hong (Dept. of Health Policy & Management, Inje University)
Park, Hye-Jin (Dept. of International Healthcare Administration, Daegu Catholic University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.19, no.11, 2018 , pp. 126-136 More about this Journal
Abstract
The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.
Keywords
Stroke; Medical service quality outcome; Mortality rate; Machine learning; Prediction model; Severity-Adjustment; Comorbidity;
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