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      저자 : Hou-Chaung Chen (검색결과 1 건)
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        Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms

        Ching-Yen Kuo,Liang-Chin Yu,Hou-Chaung Chen,Chien-Lung Chan 대한의료정보학회 2018 Healthcare Informatics Research Vol.24 No.1

        Objectives: The aims of this study were to compare the performance of machine learning methods for the prediction of themedical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) andto apply these methods to explore the important factors associated with the medical costs of spinal fusion. Methods: A dataset was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients ofTw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vectormachines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA3.8.1. Results: Five hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medicalcost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The lengthof stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The randomforest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%,a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. Conclusions: Our study demonstrated that the randomforest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms ofincreasing the financial management efficiency of this operation.

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