臺灣博碩士論文加值系統

隨著健康資訊系統的日新月異和決策者及衛生部門持續的鼓勵採用電子健康記錄，醫療服務已經累積大量的病人健康資料。藉由所謂的4 V（速度，數量，種類和正確性）步驟，使得健康資料庫正在不斷擴充。這些龐大的資料是由數十億列和數百行筆資料組成。醫學資訊的專家正積極研究以數學的方法和技術去探索，將龐大的資料庫加值應用，達到足夠且有效的健康照護。本研究藉由分析龐大的健康照護資料庫，研發出一種新型的顯現方法，來檢視台灣人口和所有疾病的視覺化疾病關聯地圖。
台灣中央健康保險局在2000 年到2002 年申報統計中，總人口約有2100 萬人，其中就醫人次共有7 億 8100 萬人次。本論文在研究中所應用的研究步驟，包含觀察這些疾病的數據分析和視覺化的表現。而知識發掘跟資料探勘的技術(Knowledge Discovery and Data Mining, KDD)，被應用在找出這些觀察到的疾病之關聯強度。所以本論文創建一個基於網絡的視覺化疾病關聯(DDA)互動式地圖。 （請連結：http://disease-map.net）
本研究在基礎研究與臨床應用上搭起橋樑，運用KDD，使疾病關聯和其對應數據視覺化展現，進而創造一個互動式的疾病全基因關聯地圖。此外，這項研究將解釋疾病關聯地圖如何以生物網絡的原則進行實證醫學的驗證。這項研究開啟了一個新的陣列，從不同的領域，如基因組學，蛋白質組學，分子生物學和生物醫學資訊學去探究相關疾病的發病機制，從而精確的達到個人化 (Personalization)、參與 (Participation)、預測 (Prediction)與預防 (Prevention)的4p 醫
學，增加醫療研究人員的研究機會。

With the advancement in the Health Information Systems and a constant encouragement from the policy-makers and health authorities for adoption of the electronic health records, had has dramatically increased the patient level health data accumulated by the health providers. Health databases are constantly getting increased by 4 V’s (Velocity, Volume, Variety and Veracity). The number of rows (objects) crossing the order of N=109 (billions) and the number of columns (attributes to the objects) are growing to the order of 102 (hundreds). Informaticians are eager to explore methodologies and techniques that can make ‘meaningful’ use of huge database in order to achieve an efficient health care. This study analyzes huge datasets in order to develop a novel visualization method to view the holistic picture of all the diseases and their associations observed in the Taiwanese population.
Three years (2000-2002) claimed data from Bureau of National Health Insurance (BNHI) with about 781 million visits by approximately 21 million populations was used. The basic steps involved in this study were to analyze, represent and visualize the diseases observed among population. The
techniques of Knowledge Discovery and Data Mining (KDD) were applied to analyze data by measuring the strength of the associations among diseases observed in the population. A database was created to represent all the observed disease-disease associations (DDA). And a web-based interactive
map was created to visualize DDA database. (visit: http://disease-map.net)This study uses “bedside to bench” a reverse translational approach by applying KDD and a novel
visualization techniques on DDA database, thus resulting in a creation of an interactive disease-wide association map. In addition, this study will interpret the map by using biological network principles and recommends a few practical implications. This study opens a new array of opportunities for researchers from diverse fields like genetics, proteomics, molecular biology and biomedical informatics interested in exploring the underlying disease pathogenesis leading to precise prediction, prevention, participative and personalized (4P) medicine.

Acknowledgments i
摘要 ii
Abstract iii
Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Aims of the study and Research questions 3
1.4 Organization of the dissertation 4
Chapter 2 Literature Review 6
2.1 Knowledge discovery in database 6
2.2 Visualization 8
2.3 Network medicine from a biomedical informatics perspective 9
Chapter 3 Method 14
3.1 Problem Definition: 14
3.2 Step 1. Creating DDA database and Validating 15
3.3 Step 2. Data Visualization and Representation 23
3.4 Step 3. Applications of the Disease maps 25
Chapter 4 Results 30
4.1 How to use the map 30
Chapter 5 Discussion 37
5.1 Bureau of National Health Insurance (BNHI) 37
5.2 Odds Ratio as a measure for strength of associations 38
5.3 Disease-Disease associations (DDA) database 39
5.4 Validation of the DDA database 39
5.5 The zero problem 40
5.6 Network Medicine 41
5.7 Degree Distribution 41
5.8 Small World Phenomenon 42
5.9 Modules 44
5.10 Motifs and Centrality 47
5.11 Idiopathic diseases 47
5.12 Network medicine leads toward 4P medicine 47
Chapter 6 Conclusion 52
References 53
Appendix 57
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Shabbir, SA et al. Facebook use leads to Health-care reform in Taiwan. The Lancet, Volume 377, Issue 9783, 18 June 2011-24 June 2011, Pages 2083-2084 …………………………………………………72
S Syed-Abdul, J Scholl, P Lee, WS Jian, DM Liou, YC Li., Study on the potential for delay tolerant networks by health workers in low resource settings. Computer methods and programs in biomedicine 107 (3), 557-564………………………………………………………………………………………. 74
Shabbir, S.A., et al., Comparison of documentation time between an electronic and a paper-based record system by optometrists at an eye hospital in south India: A time-motion study. Computer Methods and Programs in Biomedicine. 2010;100: 283-288. ……………………………………….....83

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