博碩士論文 101233003 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:5 、訪客IP:18.207.108.191
姓名 陳亭妤(Ting-shu Chen)  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 心臟外科手術之相關風險因子分析
(Analysis of correlations and risk factors in heart surgeries)
相關論文
★ 細菌物種基因體中非編碼小片段核糖核酸之預測★ 從年齡動態網路探討疾病盛行率
★ 藉由比較基因表現資料研究次世代定序與晶片技術分析差異★ 啟動子甲基化與對應之基因表現微陣列資訊整合分析
★ 乾燥綜合症與非病毒型肝炎之相關因子分析★ 氣候變遷對人類疾病網路造成衝擊
★ 台北和中壢地區不孕症分佈與共病探討★ 探討台灣的門診疾病與環境空氣品質的濃度變化之相關性
★ 使用支持向量機預測蛋白質醣基化位置★ 使用基因表現資料預測基因轉錄調控網路
★ RNA Riboswitch搜尋系統之設計與實作★ 人類疾病差異表現基因與調控網路之整合系統
★ 利用赫伯特-黃轉換法辨識酵母菌在呼吸/還原週期中的震盪基因群★ 運用高通量基因微矩陣列方法解析由嗜鉻 細胞分化成神經細胞之全基因體的調控
★ 不同微陣列預處理方法以及即時聚合酶鏈鎖反應之微陣列基因表現量比較★ 利用赫伯特-黃轉換法做為在質譜儀分析技術的前處理方法
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限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。
  3. 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。

摘要(中) 心臟病占我國十大死因的第二位,影響國人的健康。目前除了服藥、氣球擴張之外,外科手術為一種治療心臟病有效的方式。此次計畫共有三種不同的手術,分別是冠狀動脈繞道手術(CABG)、更換心臟瓣膜手術(VALVE)以及主動脈手術(AO)。將這三種手術的內容個別來做分析,不同手術有不同的內容,而手術的內容主要包括了病患的基本資料,例如:生日、身高、體重、性別…等,除了基本資料外,還有病患術前、手術中及術後的資料,例如:病患血壓、服用的藥物廠牌以及次數、手術次數、病患心臟是否衰竭以及衰竭程度、是否有併發症…等,藉由這些資料可以分析出病患術前、術後的差異,或者服用不同廠牌的藥物對病患是否有影響,以及在手術過程中使用了哪些東西會影響病患生存率…等,分析這些資料後可以藉由醫生術後的用藥、病患術前以及手術過程中的狀態預測出病患術後可能的改變,除了術前、術後的差異之外,在術後是否需要二次開刀、是否有併發症以及對病患的觀察可以預測出其生存率,除了使醫生較為便利了解病患情況之外,甚至可能藉此降低病患的死亡率以及減緩醫生在手術過程中的錯誤率,對未來的心臟外科有幫助,也可以使病患以及病患家屬對手術增加信心。
摘要(英) Heart disease is the second of leading cause of death in Taiwan affect people′s health. Except for taking medicine and Percutaneous Transluminal Coronary Angioplasty (PTCA), cardiac surgery is an effective way to treat heart disease. There are three different types of surgeries which are coronary artery bypass graft surgery (CABG), heart valve surgery (VALVE) and aortic surgery (AO) here. Different surgery contains different factors, using the factors in each surgery to do data mining, the factors including patient’s basic information, such as: birthday, height, weight, gender…etc. Besides patient’s basic information, patient’s data before, during and after surgery are included, for example: blood pressure, the brand and frequency of drugs, number of surgeries, if patients with heart failure or complications or not...etc. Mining this data can analyze the different of patient’s status between before and after surgery, whether different brand of drug will influent patient or not, and using what things will affect patient’s survival...etc. After analysis of these data, doctor can estimate patients’ possible changes after surgery by postoperative medication and states before and during surgery. Not only can predict preoperative and postoperative differences, but also can estimate patients’ survival rate by their condition, such as: if the patient need secondary surgery or get complications after surgery or not. In addition to let doctors understand patients’ conditions conveniently, even are able to reduce the mortality rate of patients and error of surgery. It is good for the future of cardiac surgery and can make the surgical patients and patients′ families increase confidence of surgery.
關鍵字(中) ★ 心臟外科
★ 關聯性
★ 冠狀動脈繞道手術
★ 生存率
關鍵字(英) ★ heart surgery
★ relevance
★ relation
★ survival
★ CABG
論文目次 Chinese Abstract i
English Abstract ii
Chapter 1 Introduction 1
1-1 Background 1
1-2 Motivation 3
Chapter 2 Related Works 4
2-1 Surgery Data Analysis 4
2-2 Heart Surgery Parameter 5
Chapter 3 Material and Methods 6
3-1 Material 6
3-1-1 Data Source 6
3-2 Data Preprocessing 7
3-3 Methods 8
3-3-1 Classification Tool 8
3-3-2 Statistical Analysis 10
Chapter 4 Results 12
4-1 Data Cleaning 12
4-2 Relevance between Survival and Ventilator >7 Days and In Hospital Mortality and Last F/U Date 13
4-3 Relevance in CABG 16
4-3-1 Known Relevance 16
4-3-2 Relevance between Uremia and Drain Day 20
4-3-3 Relevance between Post Op IABP and LOIS and Post LVEF and Survival 23
4-3-4 Relevance between Post LVEF and Age and In Hospital Mortality and Pre Op COPD 27
4-3-5 Relevance between Pre LVEF and Other 31
4-4 Relevance in VALVE 34
4-4-1 Known Relevance 34
4-4-2 Relevance between Cardioplegia and Troponin I 35
4-5 Relation Graph 37
Chapter 5 Discussion 43
References 46
參考文獻 1. Hsiao A (2013) Heart disease is nation′s second cause of deaths. Taipei Times.
2. 蘇大成, 杜宗禮, 王榮德 (2001) Occupation and Cardiovascular Disease--Review and Case Discussions.
3. Bridgewater B, Keogh B, Kinsman R, Walton P (2009) Sixth National Adult Cardiac Surgical Database Report 2008. The Society of Cardiothoracic Surgeons of Great Britain and Ireland (July 2009).
4. 許寬立 (2008) 冠狀動脈繞道手術. 義大醫訊.
5. Vaccarino V, Abramson JL, Veledar E, Weintraub WS (2002) Sex differences in hospital mortality after coronary artery bypass surgery evidence for a higher mortality in younger women. Circulation 105: 1176-1181.
6. Gurm HS, Whitlow PL, Kip KE (2002) The impact of body mass index onshort-and long-term outcomes inpatients undergoing coronary revascularizationinsights from the bypass angioplasty revascularization investigation (BARI). Journal of the American College of Cardiology 39: 834-840.
7. Failure M-aGGiCH (2012) The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta-analysis. European Heart Journal 33: 1750-1757.
8. Mascarenhas J, Azevedo A, Bettencourt P (2010) Coexisting chronic obstructive pulmonary disease and heart failure: implications for treatment, course and mortality. Current opinion in pulmonary medicine 16: 106-111.
9. Menzies T, Hu Y (2003) Data mining for very busy people. Computer 36: 22-29.
10. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21: 660-674.
11. Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques: Morgan kaufmann.
12. Apté C, Weiss S (1997) Data mining with decision trees and decision rules. Future Generation Computer Systems 13: 197-210.
13. Quinlan JR (1993) C4. 5: programs for machine learning: Morgan kaufmann.
14. Duda RO, Hart PE, Stork DG (2012) Pattern classification: John Wiley & Sons.
15. Goyal A, Mehta R (2012) Performance Comparison of Naïve Bayes and J48 Classification Algorithms. International Journal of Applied Engineering Research, ISSN: 0973-4562.
16. Bouckaert RR, Frank E, Hall M, Kirkby R, Reutemann P, et al. (2013) WEKA Manual for Version 3-7-10.
17. Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, et al. (1999) Weka: Practical machine learning tools and techniques with Java implementations.
18. Holmes G, Donkin A, Witten IH. Weka: A machine learning workbench; 1994. IEEE. pp. 357-361.
19. Garner SR, Cunningham SJ, Holmes G, Nevill-Manning CG, Witten IH. Applying a machine learning workbench: Experience with agricultural databases; 1995. pp. 14-21.
20. Greenwood PE (1996) A guide to chi-squared testing: John Wiley & Sons.
21. Onchiri S (2013) Conceptual model on application of chi-square test in education and social sciences. Educational Research and Reviews 8: 1231-1241.
22. Symbas P (1989) Chest drainage tubes. The Surgical clinics of North America 69: 41-46.
23. Meyer TW, Hostetter TH (2007) Uremia. New England Journal of Medicine 357: 1316-1325.
24. Krishna M, Zacharowski K (2009) Principles of intra-aortic balloon pump counterpulsation. Continuing Education in Anaesthesia, Critical Care & Pain 9: 24-28.
25. POMBO JF, TROY BL, RUSSELL RO (1971) Left ventricular volumes and ejection fraction by echocardiography. Circulation 43: 480-490.
26. Gutierrez C, Blanchard DG (2011) Atrial fibrillation: diagnosis and treatment. American family physician 83.
27. Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, et al. (2010) Guidelines for the management of atrial fibrillation The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). European heart journal 31: 2369-2429.
28. Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS (2012) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. American journal of respiratory and critical care medicine 163.
29. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, et al. (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. American journal of respiratory and critical care medicine 176: 532-555.
30. Adams Jr, Bodor GS, Davila-Roman VG, Delmez J, Apple F, et al. (1993) Cardiac troponin I. A marker with high specificity for cardiac injury. Circulation 88: 101-106.
指導教授 吳立青(Li-ching Wu) 審核日期 2014-7-18
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明