臺灣博碩士論文加值系統

傳染病偵測系統是面臨全球新興傳染病與生物恐怖攻擊活動時，最重要的一道公共衛生防線。由於過去傳統的傳染病偵測系統對公共衛生單位而言，屬於接受醫療院所通報資訊的被動式作法，在傳染病防治的疫情掌握會有時效性較差的遺憾，自動化症候群系統能避免被動接受訊息的遺憾，並增加公共衛生防疫單位對於社區異常的警覺敏感度。
在疾病管制局疾病流行早期即時監測系統資料中發現，資料品質差的地區由於資訊不足，較難提早發現異常訊號，顯示提升系統資料品質的重要性，本研究模擬疾管局通報架構，在北市某醫院建立整合性急診症候群監視系統，建立之回饋機制將分兩部分，分別為資料品質狀況回饋機制及資料分析回饋機制，提供分析介面給使用者可以很簡單和以直覺化的方式操作查詢，並以視覺化的報表呈現他們所要的訊息，讓醫院可以掌握本身資料品質狀況，並提供各醫院或地區的疫情資訊，給醫療院所或基層衛生局所疫情調查人員，鑑別可能的異常狀況，適時的採取調查作業。

Infectious disease monitoring system is the most important line of defense in public health when facing the global rising infectious diseases and biological terroristic attacks. For the public health department, the traditional infectious disease monitoring system received the information announce from hospital passively, and could not control the situations of infectious disease as soon as possible, but the automatic one can improve these disadvantages and the epidemic prevention department can be more sensitive to the unusual situations in community.
The information of traditional infectious disease monitoring system in Center of Disease Control showed that the inferior data quality could not find out the unusual messages rapidly, so it revealed the important of data quality. This study establishes syndromic surveillance system in Taipei, and divides into two parts, one is the data quality situation feedback system, the other is the data analyze feedback system, and the system provides a friendly interface and statistic form for users, so the hospital can control the data quality and will supply the unusual messages to hospital or public health worker do the survey timely.

標題 I
審定書 II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 IX
圖目錄 IX
中文摘要 XI
英文摘要 XIII
第一章、緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
第二章、文獻探討 6
2.1 資料品質(DATA QUALITY) 6
2.1.1 資料品質的定義 6
2.1.2 資料品質的構面 6
2.1.3 資料品質處理 7
2.2 症候群監視(SYNDROMIC SURVEILLANCE) 8
2.2.1 例行監視 8
2.2.2 監視系統現況 9
2.2.3 台灣症候群監視系統 11
2.3 資訊視覺化(INFORMATION VISUALIZATION) 12
2.3.1 視覺化在醫學上的應用 12
2.3.2 視覺化互動技術介紹 12
第三章、醫院資料上傳品質監控 14
3.1 全國疾病流行早期即時監測系統資料現況分析 14
3.1.1 上傳資料描述性分析 14
3.1.2 上傳間斷分析 17
3.1.3 上傳資料品質分析 18
3.2 整合性系統資料上傳定義 28
3.3 資料上傳品質監控方法 29
3.3.1 唯一性監控(Uniqueness Detection) 29
3.3.2 值域監控(Domain Detection) 29
3.3.3 完整性監控(Completeness Detection) 31
3.3.4 值相依性監控(Value Dependency Detection) 31
3.3.5 處理不一致性方式 32
3.4 上傳機制與程序控制設計 32
3.5 台北市資料上傳品質評估 33
3.5.1 唯一性監控評估 33
3.5.2 值域監控評估 34
3.5.3 完整性監控評估 34
第四章、醫院上傳病歷資料分析 36
4.1 症候群監視的需求分析 36
4.1.1 症候群分類定義 36
4.1.2 分析工具的需求 38
4.2 資料庫與資料倉儲架構設計 38
4.2.1 多維度資料模型 39
4.2.2 OLAP多維資料操作 39
4.3 自動回饋設計 40
4.4 使用者介面 41
4.4.1 系統架構 42
4.4.2 系統設備及軟體 42
4.4.3 使用者帳號權限管理系統 43
4.4.4 疫情資訊系統 45
4.4.5 資料品質查詢系統 46
4.4.6 症候群病例數查詢 46
第五章、討論 48
5.1 資料品質討論 48
5.2 整合性系統後續應用 48
5.3 研究限制 49
第六章、結論與建議 50
6.1 結論 50
6.2 建議 51
參考資料 52
中文文獻 52
英文文獻 53
電子資料 56
附錄一、主訴代碼表 57
附錄二、急診屬性代碼表 62
附錄三、來源代碼表 63
附錄四、掛號科別代碼 64
附錄五、處理方式代碼表 65

Ballou DP and Pazer HL. Modeling Data and Process Quality in Multi-input, Multi-output Information Systems. Management Science 1985;31(2):150-162.
Chittaro L. Information visualization and its application to medicine. Artificial Intelligence in Medicine 2001;22:81-88
Chiu CH, Wu TS, Wu JS, Lu SW, Shih FY, Lung AJ et al. Syndromic Surveillance System Using Emergency Department Patients Medical Information in Taiwan：The Automation Informatics System of Data Collection and Processing. MIST2004
Espino JU, Wagner M, Szczepaniak C, Tsui FC, Su H, Olszewski R et al. Removing a Barrier to Computer-Based Outbreak and Disease Surveillance - The RODS Open Source Project. MMWR 2004;53:32-39
Gesteland PH, Gardner RM, Tsui FC, Espino JU, Rolfs RT, James BC et al. Automated Syndromic Surveillance for the 2002 Winter Olympics. Journal of the American Medical Informatics Association 2003;10(6):547-554
Hernandez MA, Stolfo SJ. The Merge/Purge Problem for Large Databases. Proceedings of the 1995 ACM SIGMOD, May 1995.
Hutwagner L, Thompson W, Seeman GM, Treadwell T. The Bioterrorism Preparedness and Response Early Aberration Reporting System (EARS). Journal of Urban Health: Bulletin of the New York Academy of Medicine 2003;80(2):89-96
Lombardo J, Burkom H, Elbert E, Magruder S, Lewis SH, Loschen W et al. A Systems Overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II). Journal of Urban Health: Bulletin of the New York Academy of Medicine 2003;80(2):32-42
Ng V, Lee J. Quantitative Association Rules over Incomplete Data. IEEE International Conference 1998;3(3):2821 -2826
Oman RC, Ayer TB. Improving Data Quality. Journal of Systems Management 1988:31-35.
Orr K. Data Quality and System Theory. Journal A 1988;39(44):42-45
Quass D. A Framework for Research in Data Cleaning. Unpublished Manuscript. Brigham Young Univ 1999
Quinlan JR. Unknown Attribute Values in Induction. Proceedings of the 6th International Machine Learning Workshop 1989:164-168.
Sargent P. Data Quality in Materials Information Systems. Computer-Aided Design 1992;24(9):477-490
Stolte C, Tang D, Hanrahan P. Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. IEEE Transactions on Visualization and Computer Graphics 2002;8(1):52-65
The National Electronic Disease Surveillance System Working Group. National Electronic Disease Surveillance System (NEDSS): A Standards-Based Approach To Connect Public Health and Clinical Medicine. Journal of Public Health Management and Practice 2001;7(6):43-50
Tsui FC, Espino JU, Dato VM, Gesteland PH, Hutman J, Wagner MM. Technical Description of RODS: A Real-time Public Health Surveillance System. Journal of the American Medical Informatics Association 2003;10(5):1-10
Wagner MM, Espino J, Tsui FC, Gesteland P, Chapman W, Ivanov O et al. Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project. MMWR 2004;53:28-31