博碩士論文 102233006 詳細資訊




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姓名 蔡智揚(Chi-yang Tsai)  查詢紙本館藏   畢業系所 系統生物與生物資訊研究所
論文名稱 從性別、年齡動態網路研究疾病併發症
(Research disease complication from gender and age dynamic disease network)
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摘要(中) 此研究是從性別、年齡的疾病動態網路研究疾病併發症,每個疾病發生可能會隨著年紀的增加或者性別的影響而有所不同,我們利用這動態的網路圖去了解在每個年齡層的男女生分別會有甚麼併發症的出現。而人之所以會生病不光只是某個病的病發,有可能因為本身自己有得過甚麼病而影響另一個病的出現。所以利用McNemar′s test觀察兩疾病間是否有關係,以及skewness test觀察兩疾病間的方向性。
  而男生要注意生殖器的清潔,這將會影響自己在未來會不會得更多關於生殖泌尿系統的疾病。隨著年紀的增加,男生在中年期會有攝護腺增生的疾病出現,而月經的問題會一直困擾著女性,直到老年期之後問題才會有所改善,但卻會有停經之後的問題存在。
摘要(英) This research is to look into complications through dynamic disease network of gender and age, every occurrence of disease may be affected by the differentials of age or gender. Use this dynamic network to show varied complications among all age within each gender. An emerge of a certain disease may be more than a complication of another disease, previously gotten diseases may lead to its emergence. Therefore, we applied McNemar′s test to observe correlations between each disease, and skewness test to obtain theirdirection.
The cleaning of male genital organ requires attention, it has impacts upon the occurrence of genital organs related diseases in the future. As age increases, male may have benign prostatic hyperplasia related diseases at mid ages. While menstruated issues have always been a concern of female′s, not until old ages will these problems be solved, yet elders have to deal with amenorrhea.
關鍵字(中) ★ 疾病網路
★ 動態網路
★ 偏度檢定
★ 因果關係
關鍵字(英)
論文目次 Chinese Abstract i
English Abstract ii
Table of Contents iv
List of figures vi
List of tables vii
Chapter 1 Introduction 1
1-1 Background 1
1-2 Motivation 2
Chapter 2 Material and methods 2
2-1 Material 2
2-1-1 Medical record 2
2-1-2 ICD-9-CM codes 3
2-2 Methods 4
2-2-1Hypothesis 4
2-2-2 McNemar′s test 4
2-2-3 Skewness test 6
2-2-4 Odd ratio 6
2-3 Data processing 7
Chapter 3 Results 10
3-1 Medical division 10
3-2 Age 11
3-3 Gender 12
3-4 Focus on disease 13
3-5 Case study: genitourinary system 15
Chapter 4 Discussion 22
References 23
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3. Fleiss, J.L., B.A. Levin, and M.C. Paik, Statistical methods for rates andproportions. 3rd ed. Wiley series in probability and statistics. 2003,Hoboken, N.J.: J. Wiley. xxvii, 760 p.
4. Vanunu, O., et al., Associating Genes and Protein Complexes with Disease via Network Propagation. PLoS Computational Biology, 2010. 6(1): p.e1000641
5. Goh, K.-1., et al., The human disease network. PNAS, 2007. 104(21): p.8685-8690.
6. Buck, C.J., 2001 ICD-9-CM, volumes 1, 2, and 3. 2003.
7. OSVOLL, P.O. and T. WOLDBaK, Distribution and skewness of occupational exposure sets of measurements in the Norwegian industry. Elsevier Science Ltd, 1999. 43(6): p.421-428.
8. Kraemer, H.C., Reconsidering the odds ratio as a measure of 2x2 association in a population. Stat Med, 2004. 23(2): p.257-70
9. Knapp, R.G. and M.C. Miller, Clinical epidemiology and biostatistics. National medical series for independent study. 1992, Malvern, Pa; Baltimore London : Harwal Pub. Co; Williams &Wilkins.Xi, 435p.
10. Wassertheil-Smoller, S., Biostatistics and epidemiology : a primer for health and biomedical professionals. 3rd ed. 2004, New York: Springer-Verlag. Xvi, 243p.
11. Oliva, R., Novials, A., Sanchez, M., Villa, M., Ingelmo, M., Recasens, M., Ascaso, C., Bruguera, M., Gomis, R. The HFE gene is associated to an earlier age of onset and to the presence of diabetic nephropathy in diabetes mellitus type 2. Endocrine 24: 111-114, 2004.
12. Douglas, A. S., et al. (1991). Composition of Seasonality of Disease. Sage journals 36 no. 3: 76-82.
13. Yildirim, M. A., et al. (2007). "Drug-target network." Nat Biotechnol 25(10): 1119-1126.
14. Carl R, Tom SC, Mark WJ, et al.: Diabetic Retinopathy. In: AAO, ed. Retina and Vitreous. San Francisco, American Academy of Ophthalmology, 2007:99-119.
指導教授 吳立青 審核日期 2015-7-23
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