1. NCCU Academic Hub
  2. 學術產出
  3. 商學院
  4. 學位論文
  5. Item

學術產出-學位論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 運用Benford定律的智慧型健保費用異常偵測模型之研究
An intelligent model of detecting anomalous health-insurance expenses using Benford`s Law
作者 楊喻翔
貢獻者 姜國輝
楊喻翔
關鍵詞 Benford定律
健保費用
申報異常
計算智慧
班福定律
日期 2011
上傳時間 30-十月-2012 11:21:11 (UTC+8)
摘要 目前健保局所能查核到的違規案件來源有五項，即民眾檢舉、投保單位經辦人檢舉、審查費用時發現異常而移辦、專案稽查、繳回之健保卡發現異常。但只有審查費用流程應用電腦檔案分析可透過大量的資料分析方法篩選出異常院所。然而，電腦檔案分析只能偵測醫師的服務量是否「偏離常態分配」，亦即只能偵查出某些醫師或院所可能做了過多不必要的服務，而無法偵測出虛報或詐欺等行為。
     因而，本研究透過大量詐欺文獻回顧，發現其中Bolton & Hand (2002)指出一個最佳的例子為應用Benford定律的數字分析。Benford定律即是凡符合此法則的資料中，其第一位數的值越小者則出現的頻率就越大，而數值越大者出現的機率就越小。近幾年，Benford定律被應用在不同領域的舞弊或詐欺的審查流程中。
     由於目前尚未有專文探討運用Benford定律於臺灣健保醫療費用異常之相關研究。本研究以Benford定律為基礎，利用健保研究資料庫的1999至2003年住院全部及門診抽樣的申報資料進行實證，步驟上有三：一、進行全體住院及門診機構的整體實證，二、檢視單獨以數字分析法是否可以找出異常機構，三、提出一個智慧型費用異常偵測模型並實證其效果。
     本研究結論有三：
     一、健保特約機構中，住院機構的健保費申請數字符合第一位數的Benford定律，第二、第三及第四位位數不符合。而其中的一般費用部分符合第一、二、三、四位數的Benford定律，論病計酬案件則只有第一位數符合。至於健保費的申請數字之第二、第三及第四位數不符全之原因為論病計酬案件不符合Benford定律，此乃因為論病計酬案件之特殊計價方式所造成。
     二、本研究指出單獨應用Benford定律的數字分析方法檢驗的確能找出異常院所，但同時也容易將正常院所誤判為異常，在利用卡方檢定、Cramer’s V統計值判斷法，無論是住院或門診機構，由於鑑別度不高造成整體正確率不佳，由此可推論單純利用數字分析法不足以檢驗出異常院所，因此需要再進一步結合其他工具。
     三、本研究所建構的智慧型費用異常偵測模型，是以GHSOM類神經網路進行變數選取工作，找出數個變數群組後，分別利用RBFNN(徑向基類神經網路)、GRNN（通用迴歸類神經網路）及ERNN（Elman反饋式類神經網路）等進行異常院所預判，並以逐步邏輯斯迴歸模型作為Benchmark，結果是以逐步邏輯斯迴歸模型所構建的線性模型得到比較好的效果，本研究推論原因可能為應用Benford定律的衍生指標和異常/正常院所之間就存在線性關係，因此可以利用邏輯斯迴歸模型來預判，並利用類神經網路模型加以佐證之。
     因此，本研究希望利用Benford定律的計算智慧技術能運用於健保資料庫，進行大規模電腦初步審查，找出更多不良醫療院所之異常申報之來源，以提供實地查核進而查到真正違規之醫療院所，如此可遏止醫療院所之犯意，進而節省健保支出，健全其財務收支平衡，為健保永續經營貢獻一份心力。
There are five sources of illegal medical cases checked by BNHI (Bureau of National Health Insurance): reported by public, reported by the operator of insured unit, unusual findings while auditing expenses, special case audit, and unusual findings for returned health insurance cards. The abnormal medical institutions can only be found out by analyzing digital data in the source of auditing expenses. However, the digital data can only detect whether the physicians’ service deviates from the normal distribution (excessive unnecessary service offered by some physicians or hospitals), instead of the false claim of medical expenses and fraud behavior.
     Thus, by reviewing a lot of fraud literature, this study finds the best example in Bolton & Hand (2002) is digit analysis of Benford`s Law. Benford`s Law points that the smaller the first digit is, the more frequent the digit shows, vice versa. In recent years, Benford`s law has been applied in fraud review process in different fields.
     So far no specific article has applied Benford`s Law in the research related to the BNHI medical expenses, so we did a study using inpatient (total) and outpatient (sampling) data from 1999 to 2003. There are three steps in this study: 1. Overall empirical study of all inpatient and outpatient medical institutions. 2. Try to find out the unusual medical institutions only using digit analysis. 3. Find out a smart anomaly detection model and verify its effectiveness.
     There are three conclusions of the study:
     1. For the health insurance expenses applied by the BNHI-contracted inpatient institutions, the frequency of the first digit accords with Benford`s Law, while the second, third, and fourth digits does not accord with Benford`s Law. For the general health insurance expense, the frequencies of the first, second, third, and fourth digit accord with Benford`s Law. While only the first digit meets Benford`s Law for cases paid by disease, as its special pricing method causes the different frequencies of the second, third, and fourth digits of health insurance expenses.
     2. This study shows that the digit analysis of Benford`s Law does contribute to find out the abnormal institutions, but also pay the price of misidentify the normal institutions. By using chi-square test and Cramer`s V statistics method, the low discrimination rates of both inpatient and outpatient hospitals leads to poor overall accuracy. It suggests that the simple method of digit analysis is insufficient to test the abnormal institutes, and further investigation with other tools is requested.
     3. This study establishes a smart anomaly detection model of health insurance expense, which is based on variable selection with GHSOM neural networks to identify the optimal model, and then uses RBFNN (radial basis function neural network), GRNN (general regression neural network), and ERNN (Elman recurrent neural network) to predict the abnormal institutions. Comparing RBFNN, GRNN and ERNN with the stepwise logistic regression model as the Benchmark, the study concludes that the linear relationship between derived indicator of Benford`s Law and abnormal/normal institutions exits. Therefore, we can predict by logistic regression model and verify by neural network model.
     The study intends to apply the smart technology of Benford`s Law to the large-scale preliminary review of the National Health Insurance database, which can help to identify the sources of the anomaly expenses of medical institutions and find out the fraud ones. Therefore, the decreasing fraud of medical institutions will cut down the health insurance expense for financial break-even. We hope we can contribute to the sustainable development of health insurance.。
參考文獻 Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society (54:6) 2003, pp 627-635.
     Benford, F. "The law of anomalous numbers," Proceedings of the American Philosophical Society (78:4) 1938, pp 551-572.
     Berger, A., & Hill, T. P. "A basic theory of Benford`s Law," Probability Surveys (8) 2011, pp 1-126.
     Bhattacharya, S., Xu, D. M., & Kumar, K. "An ANN-based auditor decision support system using Benford`s law," Decision Support Systems (50:3), Feb 2011, pp 576-584.
     Biafore, S. "Predictive solutions bring more power to decision makers," Health management technology (20:10) 1999, p 12.
     Bolton, R. J., & Hand, D. J. "Statistical fraud detection: A review," Statistical Science) 2002, pp 235-249.
     Breiman, L. "Heuristics of instability and stabilization in model selection," The annals of statistics (24:6) 1996, pp 2350-2383.
     Broomhead, D. "Radial basis functions, multi-variable functional interpolation and adaptive networks," DTIC Document, 1988.
     
     Brown, P. J., Vannucci, M., & Fearn, T. "Bayes model averaging with selection of regressors," Journal of the Royal Statistical Society: Series B (Statistical Methodology) (64:3) 2002, pp 519-536.
     Busta, B., & Weinberg, R. "Using Benford’s law and neural networks as a review procedure," Managerial Auditing Journal (13:6) 1998, pp 356-366.
     Campanario, J. "Using neural networks to study networks of scientific journals," Scientometrics (33:1) 1995, pp 23-40.
     Carslaw, C. "Anomalies in income numbers: Evidence of goal oriented behavior," Accounting Review) 1988, pp 321-327.
     Chen, S., & Billings, S. A. "Neural networks for nonlinear dynamic system modeling and identification," International Journal of Control (56:2) 1992, pp 319-346.
     Dittenbach, M., Merkl, D., & Rauber, A. "The growing hierarchical self-organizing map," IEEE, 2000, pp. 15-19 vol. 16.
     Dittenbach, M., Rauber, A., & Merkl, D. "Uncovering hierarchical structure in data using the growing hierarchical self-organizing map," Neurocomputing (48:1-4) 2002, pp 199-216.
     Drake, P. D., & Nigrini, M. J. "Computer assisted analytical procedures using Benford`s Law* 1," Journal of Accounting Education (18:2) 2000, pp 127-146.
     Efron, B., & Tibshirani, R. An introduction to the bootstrap Chapman & Hall / CRC, 1993.
     Elman, J. L. "Finding structure in time," Cognitive science (14:2) 1990, pp 179-211.
     Engel, H. A., & Leuenberger, C. "Benford`s law for exponential random variables," Statistics & Probability Letters (63:4) 2003, pp 361-365.
     FBI "Financial crimes report to the public Fiscal Year 2009," Federal Bureau of Investigation, 2009.
     
     Fernando, T., Maier, H. R., Dandy, G. C., & May, R. "Efficient selection of inputs for artificial neural network models," in: Proceedings of MODSIM 2005 International Congress n Modelling and Simulation:Modelling and Simulation Society of Australia and New Zealand, A. Zerger and R.M. Argent (eds.), 2005, pp. 1806-1812.
     Fewster, R. M. "A Simple Explanation of Benford`s Law," American Statistician (63:1), Feb 2009, pp 26-32.
     Formann, A. K. "The Newcomb-Benford Law in its relation to some common distributions," PLoS One (5:5) 2010, p e10541.
     Friedman, C., & Wyatt, J. Evaluation methods in medical informatics Springer, New York, 1997.
     Friedman, C. P., & Wyatt, J. Evaluation methods in biomedical informatics Springer Verlag, 2006.
     Fritzke, B. "Growing grid - A self-organizing network with constant neighborhood range and adaptation strength," Neural Processing Letters (2:5) 1995, pp 9-13.
     Glaser, W. A. Paying the doctor: systems of remuneration and their effects Johns Hopkins Press, Baltimore, 1970.
     Guyon, I., Alamdari, A. R. S. A., Dror, G., & Buhmann, J. M. "Performance prediction challenge," in: IJCNN 2006 international joint conference on neural networks, 2006, pp. 1649-1656.
     Guyon, I., & Elisseeff, A. "An introduction to variable and feature selection," The Journal of Machine Learning Research (3) 2003, pp 1157-1182.
     Hans-Andreas, E., & Leuenberger, C. "Benford`s law for exponential random variables," Statistics & Probability Letters (63:4) 2003, pp 361-365.
     Hastie, T., Tibshirani, R., & Friedman, J. "The Elements of Statistical Learning. Springer," New York) 2001.
     He, H. X., Wang, J. C., Graco, W., & Hawkins, S. "Application of neural networks to detection of medical fraud," Expert Systems with Applications (13:4), Nov 1997, pp 329-336.
     Hickman, M. J., & Rice, S. K. "Digital Analysis of Crime Statistics: Does Crime Conform to Benford`s Law?," Journal of Quantitative Criminology (26:3), Sep 2010, pp 333-349.
     Hill, T. P. "A statistical derivation of the significant-digit law," Statistical Science) 1995, pp 354-363.
     Hosmer, D. W., & Lemeshow, S. Applied logistic regression Wiley-Interscience, N.Y., USA, 2000.
     John, G. H., Kohavi, R., & Pfleger, K. "Irrelevant features and the subset selection problem," Proceedings of ICML-94, 11th International Conference on Machine Learning, San Francisco, New Brunswick, NJ, 1994, pp. 121-129.
     Johnson, P. "Fraud Detection with Benford`s Law," ACCOUNTANCY IRELAND (37:4) 2005, p 16.
     Jollis, J. G., Ancukiewicz, M., DeLong, E. R., Pryor, D. B., Muhlbaier, L. H., & Mark, D. B. "Discordance of databases designed for claims payment versus clinical information systems: implications for outcomes research," Annals of internal medicine (119:8) 1993, pp 844-850.
     Jones, A. J. "New tools in non-linear modelling and prediction," Computational Management Science (1:2) 2004, pp 109-149.
     Kadane, J. B., & Lazar, N. A. "Methods and criteria for model selection," Journal of the American Statistical Association (99:465) 2004, pp 279-290.
     Kohavi, R., & John, G. H. "Wrappers for feature subset selection," Artificial intelligence (97:1-2) 1997, pp 273-324.
     Kohonen, T. "Self-organized formation of topologically correct feature maps," Biological cybernetics (43:1) 1982, pp 59-69.
     Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., & Saarela, A. "Self organization of a massive document collection," IEEE Transactions on Neural Networks (11:3) 2000, pp 574-585.
     Laine, S., & Simila, T. "Using SOM-based data binning to support supervised variable selection," in: Neural Information Processing, N.R. Pal, N. Kasabov, R.K. Mudi, S. Pal and S.K. Parui (eds.), Springer-Verlag Berlin, Berlin, 2004, pp. 172-180.
     Lavrac, N. "Selected techniques for data mining in medicine," Artificial intelligence in medicine (16:1) 1999, pp 3-23.
     Lien, H. M., Chou, S. Y., & Liu, J. T. "Hospital ownership and performance: Evidence from stroke and cardiac treatment in Taiwan," Journal of health economics (27:5) 2008, pp 1208-1223.
     Lin, C. H., Lin, C. M., & Hong, C. W. "The development of dentist practice profiles and management," Journal of Evaluation in Clinical Practice (15:1), Feb 2009, pp 4-13.
     Liou, F. M., Tang, Y. C., & Chen, J. Y. "Detecting hospital fraud and claim abuse through diabetic outpatient services," Health Care Management Science (11:4), Dec 2008, pp 353-358.
     Liu, Z. X., & Xu, J. F. "Musical Instrument Audio Identification Based on Kernel Logistic Regression," 管理科學與統計決策 (7:1) 2010, pp 88-92.
     Lu, F., & Boritz, J. E. "Detecting fraud in health insurance data: Learning to model incomplete Benford`s law distributions," in: Machine Learning: Ecml 2005, Proceedings, J. Gama, R. Camacho, P. Brazdil, A. Jorge and L. Torgo (eds.), Springer-Verlag Berlin, Berlin, 2005, pp. 633-640.
     Lu, F., Boritz, J. E., & Covvey, D. "Adaptive Fraud Detection using Benford`s Law," in: Advances in Artificial Intelligence, Proceedings, L. Lamontagne and M. Marchand (eds.), Springer-Verlag Berlin, Berlin, 2006, pp. 347-358.
     Luque, B., & Lacasa, L. "The first-digit frequencies of prime numbers and Riemann zeta zeros," Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science (465:2107) 2009, p 2197.
     Maher, M., & Akers, M. "Using Benford`s Law to Detect Fraud in the Insurance Industry," International Business & Economics Research Journal (1:7) 2002, pp 21-32.
     Milley, A. "Healthcare and data mining," Health management technology (21:8) 2000, pp 44-45.
     Moody, J., & Darken, C. J. "Fast learning in networks of locally-tuned processing units," Neural computation (1:2) 1989, pp 281-294.
     Nigrini, M. J. "The detection of income tax evasion through an analysis of digital frequencies," Dissertation, Cincinnati, OH: University of Cincinnati, 1992.
     Nigrini, M. J. "A taxpayer compliance application of Benford`s law," The Journal of the American Taxation Association (18:1) 1996, pp 72-91.
     Nigrini, M. J. Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations Wiley, 2011.
     Nigrini, M. J., & Miller, S. J. "Data Diagnostics Using Second-Order Tests of Benford`s Law," Auditing-a Journal of Practice & Theory (28:2), Nov 2009, pp 305-324.
     Nigrini, M. J., & Mittermaier, L. J. "The use of Benford`s law as an aid in analytical procedures," Auditing (16) 1997, pp 52-67.
     Nisbet, R. "Data mining tools: which one is best for CRM? Part 3,"2006, http://www.information-management.com/specialreports/20060321/1049954-1.html.
     Noyons, E. C. M., & van Raan, A. F. J. "Monitoring scientific developments from a dynamic perspective: Self-organized structuring to map neural network research," Journal of the American Society for Information Science (49:1), Jan 1998, pp 68-81.
     Oakley, S. "Data mining, distributed networks, and the laboratory," Health management technology (20:5) 1999, p 26.
     Parzen, E. "On estimation of a probability density function and mode," The annals of mathematical statistics (33:3) 1962, pp 1065-1076.
     Price, M., & Norris, D. M. "Health care fraud: physicians as white collar criminals?," Journal of the American Academy of Psychiatry and the Law Online (37:3) 2009, p 286.
     Quick, R., & Wolz, M. "Benford`s law in German financial statements," Publications of Darmstadt Technical University, Institute for Business Studies (BWL)) 2005.
     Rauber, A., Merkl, D., & Dittenbach, M. "The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data," IEEE Transactions on Neural Networks (13:6) 2002, p 1331.
     Rossi, F., Lendasse, A., François, D., Wertz, V., & Verleysen, M. "Mutual information for the selection of relevant variables in spectrometric nonlinear modelling," Chemometrics and Intelligent Laboratory Systems (80:2) 2006, pp 215-226.
     Shalvi, D., & DeClaris, N. "An unsupervised neural network approach to medical data mining techniques," IEEE, 1998, pp. 171-176 vol. 171.
     Shih, J., Chang, Y., & Chen, W. "Using GHSOM to construct legal maps for Taiwan`s securities and futures markets," Expert Systems with Applications (34:2) 2008, pp 850-858.
     Similä, T. "Advances in variable selection and visualization methods for analysis of multivariate data," in: Dissertations in Computer and Information Science, Helsinki University of Technology, 2007.
     Simila, T., & Laine, S. "Visual approach to supervised variable selection by self-organizing map," International Journal of Neural Systems (15:1-2), Feb-Apr 2005, pp 101-110.
     Skousen, C. J., Guan, L., & Wetzel, T. S. "Anomalies and unusual patterns in reported earnings: Japanese managers round earnings," Journal of International Financial Management & Accounting (15:3) 2004, pp 212-234.
     Specht, D. F. "Probabilistic neural networks," Neural networks (3:1) 1990, pp 109-118.
     Specht, D. F. "A general regression neural network," Neural Networks, IEEE Transactions on (2:6) 1991, pp 568-576.
     Thomas, J. K. "Unusual patterns in reported earnings," Accounting Review) 1989, pp 773-787.
     Tibshirani, R. "Regression shrinkage and selection via the lasso," Journal of the Royal Statistical Society. Series B (Methodological)) 1996, pp 267-288.
     Torra, V., Miyamoto, S., & Lanau, S. "Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system," Information Processing & Management (41:3) 2005, pp 587-598.
     Tsumoto, S. "Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic," Information Sciences (124:1-4) 2000, pp 125-137.
     Vapnik, V. Statistical learning theory John Wiley and Sons, New York, 1998.
     Yang, Y. H., & Tsaih, R. H. "An Investigation of Research on Evolution of Altruism using Informetric Methods and the Growing Hierarchical Self-Organizing Map," Malaysian Journal of Library & Information Science (15:3) 2010, pp 1-17.
     Yang, Y. H., Tsaih, R. H., & Bhikshu, H. "The Research of Multi-Layer Topic Map Analysis using Co-word Analysis with Growing Hierarchical Self-organizing Map," International Journal of Digital Content Technology and its Applications (5:3) 2011, pp 355-363.
     中央健康保險局 "全民健康保險法第七十二條規定違法案件函送偵辦注意事項,"2007, http://dohlaw.doh.gov.tw/Chi/FLAW/FLAWDAT0202.asp?lsid=FL040430.
     
     中央健康保險局 "99年全民健康保險統計,"2010a, http://www.nhi.gov.tw/webdata/webdata.aspx?menu=17&menu_id=662&WD_ID=698&webdata_id=4004.
     
     中央健康保險局 "Tw-DRGs 審查制度說明," 中央健康保險局,2010b, http://www.nhi.gov.tw/Resource/webdata/Attach_15068_2_3_審查制度(990112).pdf.
     
     中央健康保險局 "面對健保財務的真相,"2010c, http://www.doh.gov.tw/CHT2006/DM/DM2_p01.aspx?class_no=25&now_fod_list_no=10977&level_no=2&doc_no=75029.
     中央健康保險局 "2003 全民健康保險統計動向,"2011a, www.nhi.gov.tw/Resource/webdata/ Attach_全民健康保險統計動向-2003年.pdf.
     
     中央健康保險局 "Tw-DRG審查作業問答集(100.03.21更新),"2011b,
     中央健康保險局 "全民健康保險醫事服務機構醫療服務審查辦法,"2011c, http://dohlaw.doh.gov.tw/Chi/FLAW/FLAWDAT0201.asp.
     
     中央健康保險局 "全民健康保險醫療費用審查注意事項,"2011d, http://www.nhi.gov.tw/webdata/webdata.aspx?menu=20&menu_id=710&WD_ID=813&webdata_id=1787.
     
     王濟川, & 郭志剛 Logistic 迴歸模型-方法及應用 五南, 台北, 2003.
     何清松, & 廖宏恩 "知識經濟時代醫療資訊系統之整合與規劃－利用e化資訊系統節制不必要的醫療浪費," 九十一年度委託研究計畫(DOH91-NH-1034), 台北，行政院衛生署,2003,
     吳國禎 "資料探索在醫學資料庫之應用," 中原大學醫學工程學系碩士論文, 1999.
     李玉春, 賴美淑, & 盛培珠 "影響醫療費用上漲的因素探討," 中臺學報：醫護卷 (14) 2003, pp 121–136.
     李志宏, & 施肇榮 "醫療法律案例解讀系列 10 虛報醫療費用及行政處分," 臺灣醫界 (53:1) 2010, pp 29-34.
     李俊霖 "中西醫腦中風醫療相關性探討," 長庚大學資訊管理研究所碩士論文, 2003.
     周宣光, 朱惠中, & 王復中 "健保醫療費用審查自動化之研究," 醫療資訊雜誌 (12) 2000, pp 63-96.
     周麗芳, 郎慧珠, & 紀駿輝 "全民健保醫療費用成長趨勢及其影響因素之分析," 中央健康保險局八十九年度委託研究計畫(DOH89-CA-1001),2001,
     周麗芳, 陳孝平, & 紀駿輝 "影響全民健康保險醫療費用因素之探討 (供給面)," 中央健康保險局八十七年度委託研究計畫(DOH87-NH-028),1999,
     林炳文 "供給面誘發需求與醫療保健支出的關聯性," 醫務管理期刊 (7:2) 2006, pp 137-154.
     林虹榕 "健保詐欺型態之研究," 國立台北大學犯罪學研究所碩士論文, 台北, 2008.
     林國明 "國家與醫療專業權力: 台灣醫療保險體系費用支付制度的社會學分析," 台灣社會學研究 (1) 1997, pp 77-136.
     林華卿 "以地院判決結果探討我國全民健保之醫療詐欺行為," 國立陽明大學醫務管理研究所碩士論文, 台北, 2008.
     林頌堅 "利用自組織映射圖技術的 研究主題視覺呈現 及其在資訊傳播學領域的應用," 圖書資訊學研究 (5:1) 2010, pp 23-49.
     林鳳儀, & 蘇信安 "自願性資訊揭露與強制性資訊揭露之盈餘管理," 管理學報 (28:4) 2011, pp 345-359.
     姜國輝, & 楊喻翔 "應用增長層級式自我組織映射圖於歷年研究主題圖之呈現," 圖書資訊學研究 (6:2) 2012, p (本論著未刊登但已被接受).
     洪乙禎, & 林錦鴻 "從患者就醫場所的選擇看轉診制度之落實," 社會科學論叢 (2:1) 2008, pp 61-89.
     張斐章, 張麗秋, & 黃浩倫 類神經網路-理論與實務 東華, 台北, 2003.
     章殷超 "全民健康保險醫療服務審查問題之探討," 臺灣醫學 (7:1) 2003, pp 104-114.
     莊宗南, 龔榮源, & 陳俊龍 "以資料探勘技術建立病患就醫導引-以胃腸科病患為例," 醫療資訊雜誌 (15:1) 2006, pp 17-34.
     莊逸洲, 盧成皆, & 陳理 "論量計酬與論病例計酬之支付制度對費用結構與品質之影響: 以長庚醫院之剖腹生產與陰道分娩為例," 中華公共衛生雜誌 (16:2) 1997, pp 149-159.
     許績天, & 連賢明 "賺得越少, 洗得越多?-台灣血液透析治療的誘發性需求探討," 經濟論文叢刊 (35:4) 2007, pp 415-450.
     許績天, 韓幸紋, 連賢明, & 羅光達 "部分負擔調整對醫療利用的衝擊: 以 2005 年政策調整為例," 臺灣公共衛生雜誌 (30:4) 2011, pp 326-336.
     連賢明 "如何使用健保資料進行經濟研究," 經濟論文叢刊 (36:1) 2008, pp 115-143.
     連賢明 "如何使用健保資料推估社經變數," 人文及社會科學集刊 (23:3) 2011, pp 371-398.
     郭振宗 "微生物類別診斷與抗生素用藥決策支援系統," 國立屏東科技大學資訊管理系碩士論文, 1999.
     陳孝平 "從「資訊不對稱」看全民健保規範," 國家政策論壇 (1:10) 2001, p 178~179.
     陳垂呈 "疾病診斷異常之偵測: 關聯規則之應用," 輔仁管理評論 (17:1) 2010, pp 121-141.
     陳垂呈, & 陳宗義 "利用資料探勘技術偵測疾病之異常診斷," 高雄師大學報:25) 2008, pp 47-64.
     陳建志, & 黃純德 "總額支付制度下檔案分析的轉機與危機," 臺灣牙醫界 (7:6) 2003, pp 914-918.
     陳建勝, 林佳慧, 陳美菁, & 王安平 "我國全民健康保險醫療費用審查制度之研究," 朝陽商管評論 ( 5:1) 2006, pp 111-130.
     陳團景 "健保醫療費用審查之標準化與制度化," in: 全民健保醫事服務機構醫療服務專業審查研討會, 1997.
     陳儒賢, 陳清田, 潘衍谷, & 林典蔚 "結合自組織映射圖網路與輻狀基底函數網路於地下水位預測之研究," 農業工程學報 (55:2) 2009, pp 42-55.
     陳錦烽, 許芳榮, & 陳育成 "中央健康保險局電腦稽核整體架構與推動方案之研究," DOH89-NH-034, 中央健康保險局八十九年度委託研究計畫(DOH89-NH-034), 2000.
     
     傅懷慧 "全民健康保險醫療費用給付稽核流程之研究," in: 企業管理學系, 國立中山大學, 2004.
     湯玲郎, & 林信忠 "資料萃取法在健保費用稽核之研究," 醫療資訊雜誌 (11) 2000, pp 85-104.
     湯澡薰, 郭乃文, & 張維容 "全民健保醫療服務使用公平性之探討," 醫護科技學刊 (4:4) 2002, pp 291-304.
     程仁宏, 劉玉山, & 錢林慧君 "健保局稽核查察健保，遭監察院糾正," 監察院公告訊息,2010, http://www.cy.gov.tw/sp.asp?xdURL=./di/Message/message_1.asp&ctNode=903&msg_id=2884.
     
     黃文鴻 "全民健保藥品給付範圍及藥價基準之研究," 行政院經建會委託研究計畫, 台北,1990,
     黃振宇 醫界黑幕 新苗文化, 台北, 2002.
     黃森壕 "腦中風中醫證型與西醫因子之關連性探勘研究," 長庚大學資訊管理研究所碩士論文, 2004.
     黃煌雄, 沈美真, & 劉興善 "我國全民健康保險總體檢," 監察院, 2011.
     楊喻翔, & 釋惠敏 "安寧療護文獻之計量研究: 1952-2009," 安寧療護雜誌 (16:1) 2011, pp 42-61.
     楊漢湶 "全民健康保險醫療費用協定委員會第三次委員會議紀錄,"1997, www.doh.gov.tw/ufile/doc/01~05次委員會議紀錄-911016.doc.
     
     葉怡成 應用類神經網路 儒林圖書公司, 台北, 1997.
     榮泰生 SPSS 與研究方法 五南, 台北, 2006.
     劉在銓, & 葉鑫亮 健保下的陰影：健保醫療 35 項違法實錄公開 商周出版, 台北, 2000.
     蔣肇慶 "定額支付制度下病例醫令之合適性研究," 國立中央大學資訊管理研究所博士論文, 2004.
     蔣肇慶, & 林熙禎 "資料開採在醫療資訊的研究," 醫療資訊雜誌 (9) 1999, pp 71-92.
     蔣肇慶, & 林熙禎 "論病例計酬下醫令執行項目內容之合理性研究－APORES 模式," 醫療資訊雜誌 (14) 2002, pp 1-16.
     盧瑞芬, 湯明哲, & 黃月桂 "全民健康保險經營體制之評估與研究," 中央健康保險局八十五年度委託研究計畫(DOH85-NH-001), 1996.
     
     盧瑞芬, & 謝啟瑞 醫療經濟學 學富文化, 台北, 2005.
     駱至中, 王鄭慈, 林錦昌, & 戴丁榮 "應用遺傳模糊專家分類系統於健保醫療費用申報異常行為之自動化檢測," 計量管理期刊 (2:1) 2005, pp 15-26.
     藍中賢, & 詹前隆 "結合模糊及合理論與貝氏分類法之資料探勘技
     術," in: 第十一屆全國資訊管理學術研討, 中山大學, 高雄, 2000.
     魏怡嘉, 王昶閔, 葛祐豪, & 楊菁菁 "高醫A健保 須吐回1.5億元 " in: 自由時報, 台北, 2010.
     魏慶國, 吳欣宸, 黃渝珊, 王小玲, & 吳貞慧 "醫院醫師轉診行為與轉診制度相關因素分析," 亞東學報 (26) 2006, pp 231-240.
     羅華強 類神經網路-Matlab的應用 高立圖書有限公司, 台北, 2005.
     譚令蒂, 洪乙禎, & 謝啓瑞 "論藥價差," 經濟論文叢刊 (35:4) 2007, pp 451-476.
     龐一鳴, 劉見祥, & 賴美淑 "我國醫療服務專業審查制度改革之落實方案," 台灣醫學 (7:5) 2003, pp 747-756.
描述 博士
國立政治大學
資訊管理研究所
97356504
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097356504
資料類型 thesis
dc.contributor.advisor 姜國輝zh_TW
dc.contributor.author (作者) 楊喻翔zh_TW
dc.creator (作者) 楊喻翔zh_TW
dc.date (日期) 2011en_US
dc.date.accessioned 30-十月-2012 11:21:11 (UTC+8)-
dc.date.available 30-十月-2012 11:21:11 (UTC+8)-
dc.date.issued (上傳時間) 30-十月-2012 11:21:11 (UTC+8)-
dc.identifier (其他 識別碼) G0097356504en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54556-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 97356504zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 目前健保局所能查核到的違規案件來源有五項，即民眾檢舉、投保單位經辦人檢舉、審查費用時發現異常而移辦、專案稽查、繳回之健保卡發現異常。但只有審查費用流程應用電腦檔案分析可透過大量的資料分析方法篩選出異常院所。然而，電腦檔案分析只能偵測醫師的服務量是否「偏離常態分配」，亦即只能偵查出某些醫師或院所可能做了過多不必要的服務，而無法偵測出虛報或詐欺等行為。
     因而，本研究透過大量詐欺文獻回顧，發現其中Bolton & Hand (2002)指出一個最佳的例子為應用Benford定律的數字分析。Benford定律即是凡符合此法則的資料中，其第一位數的值越小者則出現的頻率就越大，而數值越大者出現的機率就越小。近幾年，Benford定律被應用在不同領域的舞弊或詐欺的審查流程中。
     由於目前尚未有專文探討運用Benford定律於臺灣健保醫療費用異常之相關研究。本研究以Benford定律為基礎，利用健保研究資料庫的1999至2003年住院全部及門診抽樣的申報資料進行實證，步驟上有三：一、進行全體住院及門診機構的整體實證，二、檢視單獨以數字分析法是否可以找出異常機構，三、提出一個智慧型費用異常偵測模型並實證其效果。
     本研究結論有三：
     一、健保特約機構中，住院機構的健保費申請數字符合第一位數的Benford定律，第二、第三及第四位位數不符合。而其中的一般費用部分符合第一、二、三、四位數的Benford定律，論病計酬案件則只有第一位數符合。至於健保費的申請數字之第二、第三及第四位數不符全之原因為論病計酬案件不符合Benford定律，此乃因為論病計酬案件之特殊計價方式所造成。
     二、本研究指出單獨應用Benford定律的數字分析方法檢驗的確能找出異常院所，但同時也容易將正常院所誤判為異常，在利用卡方檢定、Cramer’s V統計值判斷法，無論是住院或門診機構，由於鑑別度不高造成整體正確率不佳，由此可推論單純利用數字分析法不足以檢驗出異常院所，因此需要再進一步結合其他工具。
     三、本研究所建構的智慧型費用異常偵測模型，是以GHSOM類神經網路進行變數選取工作，找出數個變數群組後，分別利用RBFNN(徑向基類神經網路)、GRNN（通用迴歸類神經網路）及ERNN（Elman反饋式類神經網路）等進行異常院所預判，並以逐步邏輯斯迴歸模型作為Benchmark，結果是以逐步邏輯斯迴歸模型所構建的線性模型得到比較好的效果，本研究推論原因可能為應用Benford定律的衍生指標和異常/正常院所之間就存在線性關係，因此可以利用邏輯斯迴歸模型來預判，並利用類神經網路模型加以佐證之。
     因此，本研究希望利用Benford定律的計算智慧技術能運用於健保資料庫，進行大規模電腦初步審查，找出更多不良醫療院所之異常申報之來源，以提供實地查核進而查到真正違規之醫療院所，如此可遏止醫療院所之犯意，進而節省健保支出，健全其財務收支平衡，為健保永續經營貢獻一份心力。		zh_TW
dc.description.abstract (摘要) There are five sources of illegal medical cases checked by BNHI (Bureau of National Health Insurance): reported by public, reported by the operator of insured unit, unusual findings while auditing expenses, special case audit, and unusual findings for returned health insurance cards. The abnormal medical institutions can only be found out by analyzing digital data in the source of auditing expenses. However, the digital data can only detect whether the physicians’ service deviates from the normal distribution (excessive unnecessary service offered by some physicians or hospitals), instead of the false claim of medical expenses and fraud behavior.
     Thus, by reviewing a lot of fraud literature, this study finds the best example in Bolton & Hand (2002) is digit analysis of Benford`s Law. Benford`s Law points that the smaller the first digit is, the more frequent the digit shows, vice versa. In recent years, Benford`s law has been applied in fraud review process in different fields.
     So far no specific article has applied Benford`s Law in the research related to the BNHI medical expenses, so we did a study using inpatient (total) and outpatient (sampling) data from 1999 to 2003. There are three steps in this study: 1. Overall empirical study of all inpatient and outpatient medical institutions. 2. Try to find out the unusual medical institutions only using digit analysis. 3. Find out a smart anomaly detection model and verify its effectiveness.
     There are three conclusions of the study:
     1. For the health insurance expenses applied by the BNHI-contracted inpatient institutions, the frequency of the first digit accords with Benford`s Law, while the second, third, and fourth digits does not accord with Benford`s Law. For the general health insurance expense, the frequencies of the first, second, third, and fourth digit accord with Benford`s Law. While only the first digit meets Benford`s Law for cases paid by disease, as its special pricing method causes the different frequencies of the second, third, and fourth digits of health insurance expenses.
     2. This study shows that the digit analysis of Benford`s Law does contribute to find out the abnormal institutions, but also pay the price of misidentify the normal institutions. By using chi-square test and Cramer`s V statistics method, the low discrimination rates of both inpatient and outpatient hospitals leads to poor overall accuracy. It suggests that the simple method of digit analysis is insufficient to test the abnormal institutes, and further investigation with other tools is requested.
     3. This study establishes a smart anomaly detection model of health insurance expense, which is based on variable selection with GHSOM neural networks to identify the optimal model, and then uses RBFNN (radial basis function neural network), GRNN (general regression neural network), and ERNN (Elman recurrent neural network) to predict the abnormal institutions. Comparing RBFNN, GRNN and ERNN with the stepwise logistic regression model as the Benchmark, the study concludes that the linear relationship between derived indicator of Benford`s Law and abnormal/normal institutions exits. Therefore, we can predict by logistic regression model and verify by neural network model.
     The study intends to apply the smart technology of Benford`s Law to the large-scale preliminary review of the National Health Insurance database, which can help to identify the sources of the anomaly expenses of medical institutions and find out the fraud ones. Therefore, the decreasing fraud of medical institutions will cut down the health insurance expense for financial break-even. We hope we can contribute to the sustainable development of health insurance.。		en_US
dc.description.tableofcontents 摘 要 II
     Abstract IV
     致謝 VI
     目錄 VIII
     圖目錄 X
     表目錄 XI
     第一章 緒論 1
     第一節　研究背景與動機 1
     第二節　研究目的 4
     第三節　研究流程 6
     名詞解釋 7
     第二章 文獻探討 9
     第一節　導致醫療浪費之可能因素 9
     壹、 健保財務短絀之現況 9
     貳、 醫療行為的特性 11
     參、 醫療浪費成因之分析 14
     第二節　醫療違規之探討 20
     壹、 醫療違規的型態 20
     貳、 醫療審查流程與異常案件來源 21
     參、 醫療費用審查之分析與困境 27
     第三節　基於Benford定律的數字分析及其應用 29
     壹、 Benford定律的數字分析技術 29
     貳、 數字分析之應用 34
     第四節　計算智慧演算法 36
     壹、 成長式階層自我組織映射模型 36
     貳、 徑向基函數神經網路 41
     參、 通用迴歸類神經網路 43
     肆、 Elman反饋式類神經網路 46
     第五節　計算智慧技術應用於醫療資訊之研究 49
     壹、 病情之診斷及醫令相關研究 50
     貳、 醫療費用申報之稽核 51
     第六節　邏輯斯迴歸理論 52
     第七節　變數選擇與模型選擇 54
     壹、 模型選擇和評估模型 54
     貳、 變數選擇的差異 55
     參、 變數選擇的不同方法 56
     第三章 研究方法 58
     第一節　研究架構與設計 58
     壹、 以Benford定律為基礎的數字分析模型之驗證 58
     貳、 結合計算智慧及數字分析法的預判模型 61
     第二節　資料來源 66
     第四章 研究結果 67
     第一節 描述統計分析 67
     第二節　醫事機構之整體分析 68
     壹、 住院醫事機構分析 68
     貳、 門診醫事機構分析 73
     第三節　數字分析模型實證結果 79
     壹、 住院醫事機構實證 79
     貳、 門診醫事機構實證 79
     第四節　結合計算智慧及數字分析法的預判模型實證結果 81
     壹、 住院醫事機構邏輯斯迴歸模型實證 81
     貳、 門診醫事機構邏輯斯迴歸模型實證 84
     參、 住院醫事機構之智慧型預判模型實證 90
     肆、 門診醫事機構之智慧型預判模型實證 92
     第五章 討論 97
     第一節　醫事機構之整體分析之討論 97
     第二節　數字分析模型實證結果之討論 100
     第三節　智慧型預判模型實證結果之討論 101
     第六章 結論與建議 106
     第一節 研究結論與貢獻 106
     第二節 研究限制與未來研究之建議 109
     參考文獻 112
     附錄 122		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097356504en_US
dc.subject (關鍵詞) Benford定律zh_TW
dc.subject (關鍵詞) 健保費用zh_TW
dc.subject (關鍵詞) 申報異常zh_TW
dc.subject (關鍵詞) 計算智慧zh_TW
dc.subject (關鍵詞) 班福定律zh_TW
dc.title (題名) 運用Benford定律的智慧型健保費用異常偵測模型之研究zh_TW
dc.title (題名) An intelligent model of detecting anomalous health-insurance expenses using Benford`s Lawen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society (54:6) 2003, pp 627-635.
     Benford, F. "The law of anomalous numbers," Proceedings of the American Philosophical Society (78:4) 1938, pp 551-572.
     Berger, A., & Hill, T. P. "A basic theory of Benford`s Law," Probability Surveys (8) 2011, pp 1-126.
     Bhattacharya, S., Xu, D. M., & Kumar, K. "An ANN-based auditor decision support system using Benford`s law," Decision Support Systems (50:3), Feb 2011, pp 576-584.
     Biafore, S. "Predictive solutions bring more power to decision makers," Health management technology (20:10) 1999, p 12.
     Bolton, R. J., & Hand, D. J. "Statistical fraud detection: A review," Statistical Science) 2002, pp 235-249.
     Breiman, L. "Heuristics of instability and stabilization in model selection," The annals of statistics (24:6) 1996, pp 2350-2383.
     Broomhead, D. "Radial basis functions, multi-variable functional interpolation and adaptive networks," DTIC Document, 1988.
     
     Brown, P. J., Vannucci, M., & Fearn, T. "Bayes model averaging with selection of regressors," Journal of the Royal Statistical Society: Series B (Statistical Methodology) (64:3) 2002, pp 519-536.
     Busta, B., & Weinberg, R. "Using Benford’s law and neural networks as a review procedure," Managerial Auditing Journal (13:6) 1998, pp 356-366.
     Campanario, J. "Using neural networks to study networks of scientific journals," Scientometrics (33:1) 1995, pp 23-40.
     Carslaw, C. "Anomalies in income numbers: Evidence of goal oriented behavior," Accounting Review) 1988, pp 321-327.
     Chen, S., & Billings, S. A. "Neural networks for nonlinear dynamic system modeling and identification," International Journal of Control (56:2) 1992, pp 319-346.
     Dittenbach, M., Merkl, D., & Rauber, A. "The growing hierarchical self-organizing map," IEEE, 2000, pp. 15-19 vol. 16.
     Dittenbach, M., Rauber, A., & Merkl, D. "Uncovering hierarchical structure in data using the growing hierarchical self-organizing map," Neurocomputing (48:1-4) 2002, pp 199-216.
     Drake, P. D., & Nigrini, M. J. "Computer assisted analytical procedures using Benford`s Law* 1," Journal of Accounting Education (18:2) 2000, pp 127-146.
     Efron, B., & Tibshirani, R. An introduction to the bootstrap Chapman & Hall / CRC, 1993.
     Elman, J. L. "Finding structure in time," Cognitive science (14:2) 1990, pp 179-211.
     Engel, H. A., & Leuenberger, C. "Benford`s law for exponential random variables," Statistics & Probability Letters (63:4) 2003, pp 361-365.
     FBI "Financial crimes report to the public Fiscal Year 2009," Federal Bureau of Investigation, 2009.
     
     Fernando, T., Maier, H. R., Dandy, G. C., & May, R. "Efficient selection of inputs for artificial neural network models," in: Proceedings of MODSIM 2005 International Congress n Modelling and Simulation:Modelling and Simulation Society of Australia and New Zealand, A. Zerger and R.M. Argent (eds.), 2005, pp. 1806-1812.
     Fewster, R. M. "A Simple Explanation of Benford`s Law," American Statistician (63:1), Feb 2009, pp 26-32.
     Formann, A. K. "The Newcomb-Benford Law in its relation to some common distributions," PLoS One (5:5) 2010, p e10541.
     Friedman, C., & Wyatt, J. Evaluation methods in medical informatics Springer, New York, 1997.
     Friedman, C. P., & Wyatt, J. Evaluation methods in biomedical informatics Springer Verlag, 2006.
     Fritzke, B. "Growing grid - A self-organizing network with constant neighborhood range and adaptation strength," Neural Processing Letters (2:5) 1995, pp 9-13.
     Glaser, W. A. Paying the doctor: systems of remuneration and their effects Johns Hopkins Press, Baltimore, 1970.
     Guyon, I., Alamdari, A. R. S. A., Dror, G., & Buhmann, J. M. "Performance prediction challenge," in: IJCNN 2006 international joint conference on neural networks, 2006, pp. 1649-1656.
     Guyon, I., & Elisseeff, A. "An introduction to variable and feature selection," The Journal of Machine Learning Research (3) 2003, pp 1157-1182.
     Hans-Andreas, E., & Leuenberger, C. "Benford`s law for exponential random variables," Statistics & Probability Letters (63:4) 2003, pp 361-365.
     Hastie, T., Tibshirani, R., & Friedman, J. "The Elements of Statistical Learning. Springer," New York) 2001.
     He, H. X., Wang, J. C., Graco, W., & Hawkins, S. "Application of neural networks to detection of medical fraud," Expert Systems with Applications (13:4), Nov 1997, pp 329-336.
     Hickman, M. J., & Rice, S. K. "Digital Analysis of Crime Statistics: Does Crime Conform to Benford`s Law?," Journal of Quantitative Criminology (26:3), Sep 2010, pp 333-349.
     Hill, T. P. "A statistical derivation of the significant-digit law," Statistical Science) 1995, pp 354-363.
     Hosmer, D. W., & Lemeshow, S. Applied logistic regression Wiley-Interscience, N.Y., USA, 2000.
     John, G. H., Kohavi, R., & Pfleger, K. "Irrelevant features and the subset selection problem," Proceedings of ICML-94, 11th International Conference on Machine Learning, San Francisco, New Brunswick, NJ, 1994, pp. 121-129.
     Johnson, P. "Fraud Detection with Benford`s Law," ACCOUNTANCY IRELAND (37:4) 2005, p 16.
     Jollis, J. G., Ancukiewicz, M., DeLong, E. R., Pryor, D. B., Muhlbaier, L. H., & Mark, D. B. "Discordance of databases designed for claims payment versus clinical information systems: implications for outcomes research," Annals of internal medicine (119:8) 1993, pp 844-850.
     Jones, A. J. "New tools in non-linear modelling and prediction," Computational Management Science (1:2) 2004, pp 109-149.
     Kadane, J. B., & Lazar, N. A. "Methods and criteria for model selection," Journal of the American Statistical Association (99:465) 2004, pp 279-290.
     Kohavi, R., & John, G. H. "Wrappers for feature subset selection," Artificial intelligence (97:1-2) 1997, pp 273-324.
     Kohonen, T. "Self-organized formation of topologically correct feature maps," Biological cybernetics (43:1) 1982, pp 59-69.
     Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., & Saarela, A. "Self organization of a massive document collection," IEEE Transactions on Neural Networks (11:3) 2000, pp 574-585.
     Laine, S., & Simila, T. "Using SOM-based data binning to support supervised variable selection," in: Neural Information Processing, N.R. Pal, N. Kasabov, R.K. Mudi, S. Pal and S.K. Parui (eds.), Springer-Verlag Berlin, Berlin, 2004, pp. 172-180.
     Lavrac, N. "Selected techniques for data mining in medicine," Artificial intelligence in medicine (16:1) 1999, pp 3-23.
     Lien, H. M., Chou, S. Y., & Liu, J. T. "Hospital ownership and performance: Evidence from stroke and cardiac treatment in Taiwan," Journal of health economics (27:5) 2008, pp 1208-1223.
     Lin, C. H., Lin, C. M., & Hong, C. W. "The development of dentist practice profiles and management," Journal of Evaluation in Clinical Practice (15:1), Feb 2009, pp 4-13.
     Liou, F. M., Tang, Y. C., & Chen, J. Y. "Detecting hospital fraud and claim abuse through diabetic outpatient services," Health Care Management Science (11:4), Dec 2008, pp 353-358.
     Liu, Z. X., & Xu, J. F. "Musical Instrument Audio Identification Based on Kernel Logistic Regression," 管理科學與統計決策 (7:1) 2010, pp 88-92.
     Lu, F., & Boritz, J. E. "Detecting fraud in health insurance data: Learning to model incomplete Benford`s law distributions," in: Machine Learning: Ecml 2005, Proceedings, J. Gama, R. Camacho, P. Brazdil, A. Jorge and L. Torgo (eds.), Springer-Verlag Berlin, Berlin, 2005, pp. 633-640.
     Lu, F., Boritz, J. E., & Covvey, D. "Adaptive Fraud Detection using Benford`s Law," in: Advances in Artificial Intelligence, Proceedings, L. Lamontagne and M. Marchand (eds.), Springer-Verlag Berlin, Berlin, 2006, pp. 347-358.
     Luque, B., & Lacasa, L. "The first-digit frequencies of prime numbers and Riemann zeta zeros," Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science (465:2107) 2009, p 2197.
     Maher, M., & Akers, M. "Using Benford`s Law to Detect Fraud in the Insurance Industry," International Business & Economics Research Journal (1:7) 2002, pp 21-32.
     Milley, A. "Healthcare and data mining," Health management technology (21:8) 2000, pp 44-45.
     Moody, J., & Darken, C. J. "Fast learning in networks of locally-tuned processing units," Neural computation (1:2) 1989, pp 281-294.
     Nigrini, M. J. "The detection of income tax evasion through an analysis of digital frequencies," Dissertation, Cincinnati, OH: University of Cincinnati, 1992.
     Nigrini, M. J. "A taxpayer compliance application of Benford`s law," The Journal of the American Taxation Association (18:1) 1996, pp 72-91.
     Nigrini, M. J. Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations Wiley, 2011.
     Nigrini, M. J., & Miller, S. J. "Data Diagnostics Using Second-Order Tests of Benford`s Law," Auditing-a Journal of Practice & Theory (28:2), Nov 2009, pp 305-324.
     Nigrini, M. J., & Mittermaier, L. J. "The use of Benford`s law as an aid in analytical procedures," Auditing (16) 1997, pp 52-67.
     Nisbet, R. "Data mining tools: which one is best for CRM? Part 3,"2006, http://www.information-management.com/specialreports/20060321/1049954-1.html.
     Noyons, E. C. M., & van Raan, A. F. J. "Monitoring scientific developments from a dynamic perspective: Self-organized structuring to map neural network research," Journal of the American Society for Information Science (49:1), Jan 1998, pp 68-81.
     Oakley, S. "Data mining, distributed networks, and the laboratory," Health management technology (20:5) 1999, p 26.
     Parzen, E. "On estimation of a probability density function and mode," The annals of mathematical statistics (33:3) 1962, pp 1065-1076.
     Price, M., & Norris, D. M. "Health care fraud: physicians as white collar criminals?," Journal of the American Academy of Psychiatry and the Law Online (37:3) 2009, p 286.
     Quick, R., & Wolz, M. "Benford`s law in German financial statements," Publications of Darmstadt Technical University, Institute for Business Studies (BWL)) 2005.
     Rauber, A., Merkl, D., & Dittenbach, M. "The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data," IEEE Transactions on Neural Networks (13:6) 2002, p 1331.
     Rossi, F., Lendasse, A., François, D., Wertz, V., & Verleysen, M. "Mutual information for the selection of relevant variables in spectrometric nonlinear modelling," Chemometrics and Intelligent Laboratory Systems (80:2) 2006, pp 215-226.
     Shalvi, D., & DeClaris, N. "An unsupervised neural network approach to medical data mining techniques," IEEE, 1998, pp. 171-176 vol. 171.
     Shih, J., Chang, Y., & Chen, W. "Using GHSOM to construct legal maps for Taiwan`s securities and futures markets," Expert Systems with Applications (34:2) 2008, pp 850-858.
     Similä, T. "Advances in variable selection and visualization methods for analysis of multivariate data," in: Dissertations in Computer and Information Science, Helsinki University of Technology, 2007.
     Simila, T., & Laine, S. "Visual approach to supervised variable selection by self-organizing map," International Journal of Neural Systems (15:1-2), Feb-Apr 2005, pp 101-110.
     Skousen, C. J., Guan, L., & Wetzel, T. S. "Anomalies and unusual patterns in reported earnings: Japanese managers round earnings," Journal of International Financial Management & Accounting (15:3) 2004, pp 212-234.
     Specht, D. F. "Probabilistic neural networks," Neural networks (3:1) 1990, pp 109-118.
     Specht, D. F. "A general regression neural network," Neural Networks, IEEE Transactions on (2:6) 1991, pp 568-576.
     Thomas, J. K. "Unusual patterns in reported earnings," Accounting Review) 1989, pp 773-787.
     Tibshirani, R. "Regression shrinkage and selection via the lasso," Journal of the Royal Statistical Society. Series B (Methodological)) 1996, pp 267-288.
     Torra, V., Miyamoto, S., & Lanau, S. "Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system," Information Processing & Management (41:3) 2005, pp 587-598.
     Tsumoto, S. "Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic," Information Sciences (124:1-4) 2000, pp 125-137.
     Vapnik, V. Statistical learning theory John Wiley and Sons, New York, 1998.
     Yang, Y. H., & Tsaih, R. H. "An Investigation of Research on Evolution of Altruism using Informetric Methods and the Growing Hierarchical Self-Organizing Map," Malaysian Journal of Library & Information Science (15:3) 2010, pp 1-17.
     Yang, Y. H., Tsaih, R. H., & Bhikshu, H. "The Research of Multi-Layer Topic Map Analysis using Co-word Analysis with Growing Hierarchical Self-organizing Map," International Journal of Digital Content Technology and its Applications (5:3) 2011, pp 355-363.
     中央健康保險局 "全民健康保險法第七十二條規定違法案件函送偵辦注意事項,"2007, http://dohlaw.doh.gov.tw/Chi/FLAW/FLAWDAT0202.asp?lsid=FL040430.
     
     中央健康保險局 "99年全民健康保險統計,"2010a, http://www.nhi.gov.tw/webdata/webdata.aspx?menu=17&menu_id=662&WD_ID=698&webdata_id=4004.
     
     中央健康保險局 "Tw-DRGs 審查制度說明," 中央健康保險局,2010b, http://www.nhi.gov.tw/Resource/webdata/Attach_15068_2_3_審查制度(990112).pdf.
     
     中央健康保險局 "面對健保財務的真相,"2010c, http://www.doh.gov.tw/CHT2006/DM/DM2_p01.aspx?class_no=25&now_fod_list_no=10977&level_no=2&doc_no=75029.
     中央健康保險局 "2003 全民健康保險統計動向,"2011a, www.nhi.gov.tw/Resource/webdata/ Attach_全民健康保險統計動向-2003年.pdf.
     
     中央健康保險局 "Tw-DRG審查作業問答集(100.03.21更新),"2011b,
     中央健康保險局 "全民健康保險醫事服務機構醫療服務審查辦法,"2011c, http://dohlaw.doh.gov.tw/Chi/FLAW/FLAWDAT0201.asp.
     
     中央健康保險局 "全民健康保險醫療費用審查注意事項,"2011d, http://www.nhi.gov.tw/webdata/webdata.aspx?menu=20&menu_id=710&WD_ID=813&webdata_id=1787.
     
     王濟川, & 郭志剛 Logistic 迴歸模型-方法及應用 五南, 台北, 2003.
     何清松, & 廖宏恩 "知識經濟時代醫療資訊系統之整合與規劃－利用e化資訊系統節制不必要的醫療浪費," 九十一年度委託研究計畫(DOH91-NH-1034), 台北，行政院衛生署,2003,
     吳國禎 "資料探索在醫學資料庫之應用," 中原大學醫學工程學系碩士論文, 1999.
     李玉春, 賴美淑, & 盛培珠 "影響醫療費用上漲的因素探討," 中臺學報：醫護卷 (14) 2003, pp 121–136.
     李志宏, & 施肇榮 "醫療法律案例解讀系列 10 虛報醫療費用及行政處分," 臺灣醫界 (53:1) 2010, pp 29-34.
     李俊霖 "中西醫腦中風醫療相關性探討," 長庚大學資訊管理研究所碩士論文, 2003.
     周宣光, 朱惠中, & 王復中 "健保醫療費用審查自動化之研究," 醫療資訊雜誌 (12) 2000, pp 63-96.
     周麗芳, 郎慧珠, & 紀駿輝 "全民健保醫療費用成長趨勢及其影響因素之分析," 中央健康保險局八十九年度委託研究計畫(DOH89-CA-1001),2001,
     周麗芳, 陳孝平, & 紀駿輝 "影響全民健康保險醫療費用因素之探討 (供給面)," 中央健康保險局八十七年度委託研究計畫(DOH87-NH-028),1999,
     林炳文 "供給面誘發需求與醫療保健支出的關聯性," 醫務管理期刊 (7:2) 2006, pp 137-154.
     林虹榕 "健保詐欺型態之研究," 國立台北大學犯罪學研究所碩士論文, 台北, 2008.
     林國明 "國家與醫療專業權力: 台灣醫療保險體系費用支付制度的社會學分析," 台灣社會學研究 (1) 1997, pp 77-136.
     林華卿 "以地院判決結果探討我國全民健保之醫療詐欺行為," 國立陽明大學醫務管理研究所碩士論文, 台北, 2008.
     林頌堅 "利用自組織映射圖技術的 研究主題視覺呈現 及其在資訊傳播學領域的應用," 圖書資訊學研究 (5:1) 2010, pp 23-49.
     林鳳儀, & 蘇信安 "自願性資訊揭露與強制性資訊揭露之盈餘管理," 管理學報 (28:4) 2011, pp 345-359.
     姜國輝, & 楊喻翔 "應用增長層級式自我組織映射圖於歷年研究主題圖之呈現," 圖書資訊學研究 (6:2) 2012, p (本論著未刊登但已被接受).
     洪乙禎, & 林錦鴻 "從患者就醫場所的選擇看轉診制度之落實," 社會科學論叢 (2:1) 2008, pp 61-89.
     張斐章, 張麗秋, & 黃浩倫 類神經網路-理論與實務 東華, 台北, 2003.
     章殷超 "全民健康保險醫療服務審查問題之探討," 臺灣醫學 (7:1) 2003, pp 104-114.
     莊宗南, 龔榮源, & 陳俊龍 "以資料探勘技術建立病患就醫導引-以胃腸科病患為例," 醫療資訊雜誌 (15:1) 2006, pp 17-34.
     莊逸洲, 盧成皆, & 陳理 "論量計酬與論病例計酬之支付制度對費用結構與品質之影響: 以長庚醫院之剖腹生產與陰道分娩為例," 中華公共衛生雜誌 (16:2) 1997, pp 149-159.
     許績天, & 連賢明 "賺得越少, 洗得越多?-台灣血液透析治療的誘發性需求探討," 經濟論文叢刊 (35:4) 2007, pp 415-450.
     許績天, 韓幸紋, 連賢明, & 羅光達 "部分負擔調整對醫療利用的衝擊: 以 2005 年政策調整為例," 臺灣公共衛生雜誌 (30:4) 2011, pp 326-336.
     連賢明 "如何使用健保資料進行經濟研究," 經濟論文叢刊 (36:1) 2008, pp 115-143.
     連賢明 "如何使用健保資料推估社經變數," 人文及社會科學集刊 (23:3) 2011, pp 371-398.
     郭振宗 "微生物類別診斷與抗生素用藥決策支援系統," 國立屏東科技大學資訊管理系碩士論文, 1999.
     陳孝平 "從「資訊不對稱」看全民健保規範," 國家政策論壇 (1:10) 2001, p 178~179.
     陳垂呈 "疾病診斷異常之偵測: 關聯規則之應用," 輔仁管理評論 (17:1) 2010, pp 121-141.
     陳垂呈, & 陳宗義 "利用資料探勘技術偵測疾病之異常診斷," 高雄師大學報:25) 2008, pp 47-64.
     陳建志, & 黃純德 "總額支付制度下檔案分析的轉機與危機," 臺灣牙醫界 (7:6) 2003, pp 914-918.
     陳建勝, 林佳慧, 陳美菁, & 王安平 "我國全民健康保險醫療費用審查制度之研究," 朝陽商管評論 ( 5:1) 2006, pp 111-130.
     陳團景 "健保醫療費用審查之標準化與制度化," in: 全民健保醫事服務機構醫療服務專業審查研討會, 1997.
     陳儒賢, 陳清田, 潘衍谷, & 林典蔚 "結合自組織映射圖網路與輻狀基底函數網路於地下水位預測之研究," 農業工程學報 (55:2) 2009, pp 42-55.
     陳錦烽, 許芳榮, & 陳育成 "中央健康保險局電腦稽核整體架構與推動方案之研究," DOH89-NH-034, 中央健康保險局八十九年度委託研究計畫(DOH89-NH-034), 2000.
     
     傅懷慧 "全民健康保險醫療費用給付稽核流程之研究," in: 企業管理學系, 國立中山大學, 2004.
     湯玲郎, & 林信忠 "資料萃取法在健保費用稽核之研究," 醫療資訊雜誌 (11) 2000, pp 85-104.
     湯澡薰, 郭乃文, & 張維容 "全民健保醫療服務使用公平性之探討," 醫護科技學刊 (4:4) 2002, pp 291-304.
     程仁宏, 劉玉山, & 錢林慧君 "健保局稽核查察健保，遭監察院糾正," 監察院公告訊息,2010, http://www.cy.gov.tw/sp.asp?xdURL=./di/Message/message_1.asp&ctNode=903&msg_id=2884.
     
     黃文鴻 "全民健保藥品給付範圍及藥價基準之研究," 行政院經建會委託研究計畫, 台北,1990,
     黃振宇 醫界黑幕 新苗文化, 台北, 2002.
     黃森壕 "腦中風中醫證型與西醫因子之關連性探勘研究," 長庚大學資訊管理研究所碩士論文, 2004.
     黃煌雄, 沈美真, & 劉興善 "我國全民健康保險總體檢," 監察院, 2011.
     楊喻翔, & 釋惠敏 "安寧療護文獻之計量研究: 1952-2009," 安寧療護雜誌 (16:1) 2011, pp 42-61.
     楊漢湶 "全民健康保險醫療費用協定委員會第三次委員會議紀錄,"1997, www.doh.gov.tw/ufile/doc/01~05次委員會議紀錄-911016.doc.
     
     葉怡成 應用類神經網路 儒林圖書公司, 台北, 1997.
     榮泰生 SPSS 與研究方法 五南, 台北, 2006.
     劉在銓, & 葉鑫亮 健保下的陰影：健保醫療 35 項違法實錄公開 商周出版, 台北, 2000.
     蔣肇慶 "定額支付制度下病例醫令之合適性研究," 國立中央大學資訊管理研究所博士論文, 2004.
     蔣肇慶, & 林熙禎 "資料開採在醫療資訊的研究," 醫療資訊雜誌 (9) 1999, pp 71-92.
     蔣肇慶, & 林熙禎 "論病例計酬下醫令執行項目內容之合理性研究－APORES 模式," 醫療資訊雜誌 (14) 2002, pp 1-16.
     盧瑞芬, 湯明哲, & 黃月桂 "全民健康保險經營體制之評估與研究," 中央健康保險局八十五年度委託研究計畫(DOH85-NH-001), 1996.
     
     盧瑞芬, & 謝啟瑞 醫療經濟學 學富文化, 台北, 2005.
     駱至中, 王鄭慈, 林錦昌, & 戴丁榮 "應用遺傳模糊專家分類系統於健保醫療費用申報異常行為之自動化檢測," 計量管理期刊 (2:1) 2005, pp 15-26.
     藍中賢, & 詹前隆 "結合模糊及合理論與貝氏分類法之資料探勘技
     術," in: 第十一屆全國資訊管理學術研討, 中山大學, 高雄, 2000.
     魏怡嘉, 王昶閔, 葛祐豪, & 楊菁菁 "高醫A健保 須吐回1.5億元 " in: 自由時報, 台北, 2010.
     魏慶國, 吳欣宸, 黃渝珊, 王小玲, & 吳貞慧 "醫院醫師轉診行為與轉診制度相關因素分析," 亞東學報 (26) 2006, pp 231-240.
     羅華強 類神經網路-Matlab的應用 高立圖書有限公司, 台北, 2005.
     譚令蒂, 洪乙禎, & 謝啓瑞 "論藥價差," 經濟論文叢刊 (35:4) 2007, pp 451-476.
     龐一鳴, 劉見祥, & 賴美淑 "我國醫療服務專業審查制度改革之落實方案," 台灣醫學 (7:5) 2003, pp 747-756.		zh_TW