Responsive image
博碩士論文 etd-0805102-125552 詳細資訊
Title page for etd-0805102-125552
論文名稱
Title
利用順序型樣於貝氏網路以建構血液透析臨床路徑
Constructing Bayesian Networks with Sequential Patterns for Hemodialysis
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
56
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2002-07-19
繳交日期
Date of Submission
2002-08-05
關鍵字
Keywords
集群、知識管理、血液透析、資料礦探採、順序型樣、貝氏網路
knowledge management, data mining, Bayesian network, Hemodialysis, clustering, sequential pattern
統計
Statistics
本論文已被瀏覽 5686 次,被下載 2390
The thesis/dissertation has been browsed 5686 times, has been downloaded 2390 times.
中文摘要
在本論文中,我們利用多變數區隔演算法來區隔血液透析臨床路徑的連續變數,並且使用集群演算法來移動時間標示,來減少貝氏網路的節點。順序型樣演算法則被用來找出可能在我們所建立的血液透析臨床路徑之貝氏網路上具有長期影響力的型樣。貝氏網路是一種利用圖形來呈現的模式,些圖形會包含每個相關聯之變數的機率值,並且可以很簡單的合併一筆新增的資料到貝氏網路中,使貝氏網路的資料可以維持最新。貝氏網路被用來呈現醫療記錄中的狀態轉移的知識。然而,鑑於貝氏網路和順序型樣演算法都只能有效的處理分散型或類別型的資料型態。我們必須利用合適的方法將資料中的連續變數加以區隔成分散型的資料型態。另外重新標示時間標示,以免除些微時間就視為不同節點的情形。經過這二個一般化的程序,我們可以改進在建構血液透析臨床路徑之貝氏網路過適化的問題。我們希望藉由我們的工作,找出的型樣,可以使專業醫療人士獲得有用的資訊,使他們在血液透析的知識管理上能有一個良好的互動結果。

Abstract
In this thesis, I introduce a multivariate discretization algorithm to discretize the continuous variables of clinical pathways of Hemodialysis and use the clustering algorithm to shift time stamps to reduce the number of nodes of Bayesian networks. The generalized sequential patterns algorithm is used to find the possible patterns, which have far-reaching effect on the next nodes of the Bayesian networks of Hemodialysis. Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest, and easily incorporates with new instances to maintain rules up to date. Bayesian networks are used to represent knowledge of frequent state transitions in medical logs. Bayesian networks and sequential patterns algorithms can only handle discrete or categorical data. Therefore, we have to discretize the continuous variables with suitable technique to generalize the node, and shift the time stamps of nodes to reduce the variations in time. With these generalizations, we improve the problem of over-fitting of the Bayesian networks of Hemodialysis. We expect the discovered patterns can give more information to medical professionals and help them to build the reciprocal cycle of knowledge management of Hemodialysis.

目次 Table of Contents
Table of Contents
ABSTRACT.....................................................I
中文摘要.....................................................II
CHAPTER 1 INTRODUCTION.....................................................1
CHAPTER 2 LITERATURE REVIEW.....................................................4
2.1 THE HEMODIALYSIS.....................................................4
2.2 DISCRETIZATION TECHNIQUE AND STUCCO.....................................................5
2.2.1 STUCCO (Search and Testing for Understandable Consistent Contrasts).....................................................5
2.2.2 MultiVariate Discretization (MVD) of Continuous Variables.....................................................6
2.3 CLUSTERING ALGORITHMS.....................................................7
2.3.1 Partitioning algorithms.....................................................8
2.3.2 Hierarchical algorithms.....................................................9
2.4 ASSOCIATION AND SEQUENTIAL PATTERNS.....................................................10
2.5 BAYESIAN NETWORK.....................................................12
CHAPTER 3 PROBLEM DEFINITION AND ANALYSIS.....................................................14
3.1 HEMODIALYSIS CLINICAL PATHS.....................................................14
3.2 BAYESIAN NETWORKS IN HEMODIALYSIS.....................................................16
CHAPTER 4 CONSTRUCTING BAYESIAN NETWORKS WITH SEQUENTIAL PATTERNS.....................................................19
4.1 PROPOSED MODEL.....................................................19
4.2 MULTIVARIATE DISCRETIZATION.....................................................21
4.3 CLUSTERING TIME WINDOWS.....................................................28
4.4 FINDING SEQUENTIAL PATTERNS IN BAYESIAN NETWORKS.....................................................34
CHAPTER 5 EVALUATING BAYESIAN NETWORKS AND PRUNED SEQUENTIAL PATTERNS.....................................................42
5.1 THE EVALUATION OF BAYESIAN NETWORKS.....................................................42
5.2 THE EVALUATION OF SEQUENTIAL PATTERNS.....................................................49
CHAPTER 6 CONCLUSIONS AND FUTURE RESEARCH.....................................................52
REFERENCES.....................................................54





參考文獻 References
References
Agrawal, R., Imielinski,T., Swami A., “Mining Associations between Sets of Items in Massive Databases”, Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington D.C., May, 1993.
Agrawal, R. and Srikant, R., “Mining Sequential Patterns”, Proceedings of International Conference on Data Engineering, Taipei, Taiwan, March, 1995.
Agrawal, R., Gehrke, J., Gunopulos, D. and Raghavan, P., ”Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications”, Proceedings of the ACM-SIGMOD International Conference on the Management of Data, pages 94-105, Seattle, Washington, June, 1998.
Bay, S. D. and Pazzani, M. J., “Detecting Change in Categorical Data: Mining Contrast Set”, Proceedings of the fifth ACM-SIGMOD International Conference on Knowledge Discovery and Data Mining, pages 302-306, August, 1999.
Bay, S. D., “Multivariate Discretization of Continuous Variables of Set Mining”, Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 315-319, August, 2000.
Brin, S., Motwani, R., Ullman, J., and Tsur, S., “Dynamic Itemset Counting and Implication Rules for Market Basket Data”, Proceedings of the International Conference on Management of Data, pages 255-264, Tucson, Arizona, June, 1997.
Chang, P.L. et al. “Use of the Transurethral Prostatectomy Clinical Path to Monitor Health Outcomes”, J of Urology. 157(1):177-83, 1997.
Evetitt, B. S., “CLUSTER ANALYSIS”, Third edition, London:Edward Arnold, 1993.
Ester, M., Kriegel, H., Sander, J., and Xu, X., ”A density-based algorithm for discovering clusters in large spatial databases with noise”, Proceedings of the second International Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August, 1996.
Garcia, J. A., Fdez-Valdivia, J., Cortijo F. J., and Molina, R., “A Dynamic Approach for Clustering Data”, Signal Processing, Vol. 44, No. 2, pages 181-196, 1994.
Hair, J., Jr., Anderson, R., Tatham R. and Black, W., “Multivariate Data Analysis”, fifth edition, Upper Saddle River, NJ: Prentice-Hall, pages 490-491, 1988.
Heckerman, D., “Bayesian networks for data mining”, Data Mining and Knowledge Discovery, 18(6): 79-119, 1997.
Kaufman, L. and Rousseeuw, P. “Finding Groups in Data: An Introduction to Cluster Analysis”, John Wiley and Sons, 1990.
Lin, F.-r., Chiu, C.-h., and Wu, S.-c., “Using Bayesian Networks for Discovering Temporal-State Transition Patterns in Hemodialysis”, Hawaii International Conference on System Sciences (HICSS-35), Hawaii, January 7-9, 2002.
Lin, F.-r., Chou, S.-c., Pan, S.-m., and Chen, Y.-m., “Mining Time Dependency Patterns in Clinical Pathways”, International Journal of Medical Informatics, 62 (1): 11-25, 2001.
MacQueen, J., ” Some methods for classification and analysis of multivariate observations”, Processing of fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pages 287-297, 1967.
National Kidney Foundation, K/DOQI, “Clinical Practice Guidelines for Hemodialysis Adequacy”, American Journal of Kidney Disease, Vol. 30, No 3, suppl 2, pages 17-66, September, 2000.
National Kidney Foundation, http://www.kidney.org/
Ng, R.T. and Han,.J., “Efficient and effective clustering methods for spatial data mining”, Processing of the VLDB Conference, Santiago, Chile, Sept, 1994.
Srikant, R. and Agrawal, R., “Mining sequential patterns: Generalizations and performance improvements”, Processing of the fifth International Conference on Extending Database Technology (EDBT), Avignon, France, March, 1996.
Titterington, D. M., Smith, A. F. and Makov, U. E.,” Statistical Analysis of Finite Mixture Distributions”, John Wiley and Sons, 1985.
Wei, C. P., Hwang, S. Y., Yang, W. S., “Mining Frequent Temporal Patterns in Process Database”, Proceedings of Workshop on Information Technologies & Systems, Brisbane, Australia, December 9-10, 2000.
Winkler, R. L., “An Introduction to Bayesian Inference and Decision”, Holt, Rinehart and Winston, Onc., Toronto, 1972.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:校內立即公開,校外一年後公開 off campus withheld
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code