應用視覺化關連性規則於醫學資料探勘—以嚴重急性呼吸道症候群的文章擷取資訊為例

Applying Visualizing Association Rules in Medical Data Mining —An Example of Information Extraction from Severe Acute Respiratory Syndrome Literatures

林祐民(Forrest Yu-Ming Lin)

指導教授 ： 蔣以仁

臺北醫學大學/醫學院/醫學資訊研究所/碩士(2003年)

摘要

本研究，我們應用視覺化的技術: Direct Graph和Hyperbolic Tree於文章的資訊擷取，並建構了一個整理文獻及呈現文件中詞與文件關連性規則的工具，此系統提供了使用者一種直覺的方式去挑選文件來節省文件閱讀和實驗設計的時間。最後，我們使用探討關於嚴重急性呼吸道症候群的文章來呈現本程式從文章中資訊的擷取和視覺化呈現的幫助。

關鍵字

視覺化 ； 關連性規則 ； 嚴重急性呼吸道症候群

並列摘要

In this research, we apply the visualization techniques: Direct Graph and Hyperbolic Tree in literature extraction. We implement the tool for researchers to rearrange the literatures and represent the association rules for each term and the documents. It provides users an intuitive way to filter the documents for saving time on paper review and experiment design. In the end, we use the documents which mentioned Severe Acute Respiratory Syndrome (SARS) to demo the literatures extraction and how visualization helps.

並列關鍵字

visualization ； association rules ； severe acute respiratory syndrome

參考文獻

Buckley, G. S. a. C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.

A. Houston, H. C., S. M. Hubbard, B. R. Schatz, T. D. Ng, R. R. Sewell, and K. M. Tolle. (1999). Medical Data Mining on the Internet: Research on a Cancer Information System. Articial Intelligence Review, 13, 437-446.

Carlos Ordonez, E. O., Levien de Braal, Cesar A. Santana, Norberto Ezquerra, Jose A. Taboada, David Cooke, Elizabeth Krawczynska, and Ernst V. Garcia. (2001). Mining Constrained Association Rules to Predict Heart Disease. Paper presented at the International Conference on Data Mining. CDC. (2003, June 4). Basic Information About SARS. Retrieved July 3, 2003, from http://www.cdc.gov/ncidod/sars/factsheet.htm Chaomei Chen, L. C. (1999). Visualizing the Evolution of a Subject Domain: A Case Study. Paper presented at the Visualization. Chen, C. (2002). editorial information visualization. Information Visualization, 1, 1-4.

Chen, C.-T. (2001). PCB WIP pattern identification and visualization using Hidden Markov Model. Christoph A. Hipke, S. S. (1999). Vega A user-centered approach to the distributed visualization of geometric algorithms. Paper presented at the Vega A user-centered approach to the distributed visualization of geometric algorithms. D. Berrar, W. D., M. Granzow, and R. Eils. (2001). Analysis of gene expression and drug activity data by knowledge-based association mining. Paper presented at the Critical Assessment of Microarray Data Analysis Techniques. Daniel Keim, M. H., Julian Ladisch, Meichun Hsu, Umeshwar Dayal. (Oct 2001). Pixel Bar Charts: A New Technique for Visualizing Large Multi-Attribute Data Sets without Aggregation. Paper presented at the IEEE InfoVis, Calif. Hung, C.-C. (2001). Visual Analysis of Production Data. Inderpal S. Bhandari, E. C., Jennifer Parker, Zachary Pines, Rajiv Pratap and Krishnakumar Ramanujam. (1997). Advanced Scout: Data Mining and Knowledge Discovery in NBA Data. Data Mining and Knowledge Discovery, 1(1), 121--125.

Jensen, S. (2001). Mining Medical Data for Predictive and Sequential Patterns. Paper presented at the Principles and Practice of Knowledge Discovery in Databases. John Lamping, R. R., Peter Pirolli. (1995). A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies. Paper presented at the Human Factors in Computing Systems. Lin, Y.-C. (2002). The Improved Self-Organizing Maps for Market Segmentation and Segments?Feature Extraction. McCormick BH, D. T. B. M. (1987). Visualization in scientific computing. Pak Chung Wong, P. W., Jim Thomas. (1999, October 24 -29).