Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers
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초록
Objectives: This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods: For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results: We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions: Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers.
Abstract
Objectives: This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods: For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results: We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions: Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers.
참고문헌 (37)
- Raphael D, "The state's role in promoting population health: public health concerns in Canada, USA, UK, and Sweden", Health Policy, 78, pp.39~55 (2006)
- 김행열, "주민유형에 따른 보건의료서비스 만족도에 관한 실증적 연구", 한국거버넌스학회보, 14(2), pp.327~350 (2007), 한국거버넌스학회
- Yoon KJ, "Using DEA to measure the efficiency of local health centers", J Korean Assoc Policy Stud, 5, pp.80~109 (1996)
- 유금록, "보건소의 생산성 측정: 전라북도를 중심으로", 한국행정학보, 37(4), pp.261~280 (2003), 한국행정학회
- Nam CH, "Policy development on health administration system in the era of local autonomous government", J Korean Soc Health Educ Promot, 16, pp.101~126 (1996)
- Chae YM, "Analysis of healthcare quality indicator using data mining and decision support system", Expert Syst Appl, 24, pp.167~172 (2003)
- Han HK, "Sequential association rules for forecasting failure patterns of aircrafts in Korean airforce", Expert Syst Appl, 36, pp.1129~1133 (2009)
- Hung SY, "Applying data mining to telecome churn management", Expert Syst Appl, 31, pp.515~524 (2006)
- Wu LG, "An expert system to predict protein thermostability using decision tree", Expert Syst Appl, 36, pp.9007~9014 (2009)
- Yeh IC, "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients", Expert Syst Appl, 36, pp.2473~2480 (2009)
- 이선미, "임상에서의 데이터 마이닝 개념과 원칙", Healthcare Informatics Research, 15(2), pp.175~189 (2009), 대한의료정보학회
- Silva A, "Mortality assessment in intensive care units via adverse events using artificial neural networks", Artif Intell Med, 36, pp.223~234 (2006)
- 최종필, "A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis", Healthcare Informatics Research, 15(1), pp.49~57 (2009), 대한의료정보학회
- Diri B, "Visualization and analysis of classifiers performance in multi-class medical data", Expert Syst Appl, 34, pp.628~634 (2008)
- Phillips-Wren G, "Mining lung cancer patient data to assess healthcare resource utilization", Expert Syst Appl, 35, pp.1611~1619 (2008)
- Chang CL, "Applying decision tree and neural network to increase quality of dermatologic diagnosis", Expert Syst Appl, 36, pp.4035~4041 (2009)
- Lee WI, "Application of neural networks to recognize profitable customers for dental services marketing: a case of dental clinics in Taiwan", Expert Syst Appl, 36, pp.199~208 (2009)
- Polat K, "Usage of class dependency based feature selection and fuzzy weighted pre-processing methods on classification of macular disease", Expert Syst Appl, 36, pp.2584~2591 (2009)
- Ture M, "The comparisons of prognostic indexes using data mining techniques and Cox regression analysis in the breast cancer data", Expert Syst Appl, 36, pp.8247~8254 (2009)
- Polun Chang, "The Feasibility of Using Classification and Identification Techniques to Auto-Assess the Quality of Health Information on the Web", Healthcare Informatics Research, 15(3), pp.247~254 (2009), 대한의료정보학회
- 강진오, "Prediction of Hospital Charges for the Cancer Patients withData Mining Techniques", Healthcare Informatics Research, 15(1), pp.13~23 (2009), 대한의료정보학회
- Exarchos TP, "Mining sequential patterns for protein fold recognition", J Biomed Inform, 41, pp.165~179 (2008)
- Chiang JH, "Discovering gene–-gene relations from sequential sentence patterns in biomedical literature", Expert Syst Appl, 33, pp.1036~1041 (2007)
- Ryan GW, "What do sequential behavioral patterns suggest about the medical decision-making process: modeling home case management of acute illnesses in a rural Cameroonian village", Soc Sci Med, 46, pp.209~225 (1998)
- Lasker GE, "Application of sequential pattern-recognition technique to medical diagnostics", Int J Biomed Comput, 1, pp.173~186 (1970)
- Lin F, "Mining time dependency patterns in clinical pathways", Int J Med Inform, 62, pp.11~25 (2001)
- Concaro S, "Temporal data mining methods for the analysis of the AHRQ archives", Proc Am Med Inform Assoc 2007 Annu Symp (2007), American Medical Information Association
- Cooper GS, "Use of SEER-Medicare data for measuring cancer surgery", Med Care, 40(8 Suppl), pp.IV43~IV48 (2002)
- Earle CC, "Identifying cancer relapse using SEER-Medicare data", Med Care, 40(8 Suppl), pp.IV75~IV81 (2002)
- Oh HS, "Health behavior and utilization of the public health center in rural adults", J Honam Univ Acad, 29, pp.361~376 (2008)
- 유혜라, "보건소 금연클리닉 활성화를 위한 방문자 이용현황과 금연성공 및 만족도 조사", 한국보건간호학회지, 22(1), pp.62~73 (2008), 한국보건간호학회
- 윤희상, "건강증진프로그램 이용과 관련된 요인 -서울시 보건소를 중심으로-", 보건사회연구, 28(2), pp.157~184 (2008), 한국보건사회연구원
- Cao JM, "Real-time decision support for integration of airline flight cancellations and delays part II: algorithm and computational experiments", Transp Plann Technol, 20, pp.201~217 (1997)
- Devaraj S, "Information technology payoff in the health-care industry: a longitudinal study", J Manage Inf Syst, 16, pp.41~68 (2000)
- Dietterich TG, "An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization", Mach Learn, 40, pp.139~157 (2000)
- Opitz D, "Popular ensemble methods: an empirical study", J Artif Intell Res, 11, pp.169~198 (1999)
- Zhu D, "A hybrid approach for efficient ensembles", Decis Support Syst, 48, pp.480~487 (2010)