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. |
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.
|