Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms
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초록
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.
Abstract
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.
참고문헌 (29)
- Rajaee SS, "Spinal fusion in the United States: analysis of trends from 1998 to 2008", Spine (Phila Pa 1976), 37(1), pp.67~76 (2012)[DOI]
- Jancuska JM, "Utilization of lumbar spinal fusion in New York State: trends and disparities", Spine (Phila Pa 1976), 41(19), pp.1508~1514 (2016)[DOI]
- Rimler SB, "Diagnosis-related groups and hospital inpatient federal reimbursement", Radiographics, 35(6), pp.1825~1834 (2015)[DOI]
- Hsiao WC, "Lessons of the New Jersey DRG payment system", Health Aff (Millwood), 5(2), pp.32~45 (1986)[DOI]
- Ugiliweneza B, "Spinal surgery: variations in health care costs and implications for episode-based bundled payments", Spine (Phila Pa 1976), 39(15), pp.1235~1242 (2014)[DOI]
- Wright DJ, "Cost variation within spinal fusion payment groups", Spine (Phila Pa 1976), 41(22), pp.1747~1753 (2016)[DOI]
- [URL]
- [URL]
- Tomar D, "A survey on data mining approaches for healthcare", Int J Biosci Biotechnol, 5(5), pp.241~266 (2013)
- 문미경, "Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities", Healthcare Informatics Research, 23(1), pp.43~52 (2017), 대한의료정보학회[DOI]
- Yahya N, "Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: a comparison of conventional and machinelearning methods", Med Phys, 43(5), pp.2040~2052 (2016)[DOI]
- Walid MS, "Economic impact of comorbidities in spine surgery", J Neurosurg Spine, 14(3), pp.318~321 (2011)[DOI]
- Chawla NV, "SMOTE: synthetic minority over-sampling technique", J Artif Intell Res, 16, pp.321~357 (2002)[DOI]
- Han J, "Data mining: concepts and techniques" (2011), Elsevier
- Eka Miranda, "Detection of Cardiovascular Disease Risk’s Level for Adults Using Naive Bayes Classifier", Healthcare Informatics Research, 22(3), pp.196~205 (2016), 대한의료정보학회[DOI]
- a Cortes C, "Support-vector networks", Machine Learning, 20(3), pp.273~297 (1995)
- Kuo PJ, "Derivation and validation of different machinelearning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan", BMJ Open, 8(1), pp.e018252~ (2018)[DOI]
- Quinlan JR, "Induction of decision trees", Mach Learn, 1(1), pp.81~106 (1986)
- Archana S, "Survey of classification techniques in data mining", Int J Comput Sci Mob Appl, 2(2), pp.65~71 (2014)
- Sanz J, "A new survival status prediction system for severe trauma patients based on a multiple classifier system", Comput Methods Programs Biomed, 142, pp.1~8 (2017)[DOI]
- Habibi S, "Type 2 diabetes mellitus screening and risk factors using decision tree: results of data mining", Glob J Health Sci, 7(5), pp.304~310 (2015)
- Breiman L, "Random forests", Mach Learn, 45(1), pp.5~32 (2001)[DOI]
- Raju D, "Exploring factors associated with pressure ulcers: a data mining approach", Int J Nurs Stud, 52(1), pp.102~111 (2015)[DOI]
- Bellazzi R, "Predictive data mining in clinical medicine: current issues and guidelines", Int J Med Inform, 77(2), pp.81~97 (2008)[DOI]
- Bradywood A, "Reduction of inpatient hospital length of stay in lumbar fusion patients with implementation of an evidence-based clinical care pathway", Spine (Phila Pa 1976), 42(3), pp.169~176 (2017)[DOI]
- Kulkarni VY, "Random forest classifiers: a survey and future research directions", Int J Adv Comput, 36(1), pp.1144~1153 (2013)
- Hu H, "A comparative study of classification methods for microarray data analysis", Proceedings of the 5th Australasian Conference on Data Mining and Analystics, pp.33~37 (2006)
- Masetic Z, "Congestive heart failure detection using random forest classifier", Comput Methods Programs Biomed, 130, pp.54~64 (2016)[DOI]
- Allyn J, "A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis", PLoS One, 12(1), pp.e0169772~ (2017)[DOI]