PL EN


Preferences help
enabled [disable] Abstract
Number of results
2014 | 68 | 6 | 449–456
Article title

Drzewa klasyfikacyjne w medycynie

Content
Title variants
EN
Classification trees in medicine
Languages of publication
PL
Abstracts
EN
The paper presents the use of computerized diagnostic decision support systems for medical diagnostics in medicine. The structure of a classical decision tree and the advantages and disadvantages of using classification trees have been discussed. Moreover, the paper deals with the effect of classification trees with respect to other classic statistical methods, such as discriminant analysis and logistic regression, taking into account the problem of variable multicollinearity and the problem of the occurrence of so-called missing data. Additionally, some examples of the application of classification trees in medicine have been shown.
PL
W pracy zaprezentowano wykorzystanie w medycynie komputerowych systemów diagnostyki medycznej. Przedstawiono budowę klasycznego drzewa decyzyjnego oraz zalety i wady stosowania drzew klasyfikacyjnych. Ponadto omówiono działanie drzew klasyfikacyjnych w świetle innych klasycznych metod statystycznych, takich jak analiza dyskryminacyjna czy regresja logistyczna, z uwzględnieniem problemu współliniowości zmiennych czy problemu występowania tzw. danych niepełnych. Podano wybrane przykłady zastosowania drzew klasyfikacyjnych w medycynie.
Discipline
Year
Volume
68
Issue
6
Pages
449–456
Physical description
References
  • 1. Garg A.X., Adhikari N.K.J., McDonald H. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. A Systematic Review. JAMA 2005; 293: 1223–1238.
  • 2. Lindgaard G., Pyper C., Frize M., Walker R. Does Bayes have it? Decision Support Systems in diagnostic medicine. Int. J. Ind. Ergon. 2009; 39: 524–532.
  • 3. Tierney W. Improving clinical decision and outcomes with information: a review. Int. J. Med. Inf. 2001; 62: 1–9.
  • 4. Berlin A., Sorani M., Sim I. A taxonomic description of computer-based clinical decision support systems. J. Biomed. Info. 2006; 39: 656–667.
  • 5. Montgomery A.A., Fahey T., Peters T.J., MacIntosh C., Sharp D.J. Evalua-tion of computer based clinical decision support system and risk chart for management of hypertension in primary care: randomised controlled trial. BMJ 2000; 320: 686–690.
  • 6. Kawamoto K., Houlihan C.A., Balas E.A., Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330: 765–772.
  • 7. Reisman Y. Computer-based clinical decision aids. A review of methods and assessment of systems. Med. Inform. 1996: 21: 179–197.
  • 8. Zupan B., Porenta A., Vidmar G., Aokin N., Bratko I., Beck J.R. Decision at hand: a decision support system on handhelds. Stud. Health Technol. Inform. 2001; 84: 566–570.
  • 9. Bagley S.C., White H., Golomb B.A. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J. Clin. Epidemiol. 2001; 54: 979–985.
  • 10. Long W.J., Griffith L.J., Selker H.P., D’Agostino R.B. A comparison of logistic regression to decision-tree induction in a medical domain. Comput. Biomed. Res. 1993; 26: 74–97.
  • 11. Huang D., Quan Y., He M., Zhou B. Comparison of linear discriminant analysis methods for the classification of cancer based on gene expression data. J. Exp. Clin. Cancer Res. 2009; 28: 149–156.
  • 12. Polat K., Günes S. A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro fuzzy inference system. Dig. Sig. Proc. 2006; 16: 913–921.
  • 13. Ji S.Y., Smith R., Huynh T., Najarian K. A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries. BMC Med. Inform. Decis. Mak. 2009; 9: 2–18.
  • 14. Arif M., Akram M.U., Minhas F.A. Pruned fuzzy K-nearest neighbor classifier for beat classification. J. Biomed. Sci. Eng. 2010; 3: 380–389.
  • 15. Verplancke T., Van Looy S., Benoit D. et al Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med. Inform. Decis. Mak. 2008; 8: 56–63.
  • 16. Chen S.T., Hsiao Y.H., Huang Y.L. et al. Comparative analysis of logistic regression, support vector machine and artificial neural network for the differential diagnosis of benign and malignant solid breast tumors by the use of three-dimensional power Doppler imaging. Korean J. Radiol. 2009; 10: 464–471.
  • 17. Lisboa P.J., Taktak A.F.G. The use of artificial neural networks in deci-sion support in cancer: A systematic review. Neural. Netw. 2006; 19: 408–415.
  • 18. Glaser J. Clinical decision support: the power behind the electronic health record. Healthc. Financ. Manage 2008; 62(7): 50–51.
  • 19. Lenz R., Reuchert M. IT support for healthcare process – premises, challenges, perspectives. Data Knowl. Eng. 2007; 61: 39–58.
  • 20. Bairstow P., Persaud J., Mendelson R., Ngyuen L. Reducing inappropriate diagnostic practice through education and decision support. Int. J. Qual in Health Care 2010; 22(3): 194–200.
  • 21. Haynes R.B., Wilczyński N.L. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision maker-research partnership systemic review. Implement. Sci. 2010; 5: 5–12.
  • 22. Thursky K.A., Buising K.L., Bak N. et al. Reduction of broad-spectrum antibiotic use with computerized decision support in an intensive care unit. Int. J. Qual Health Care 2006; 18(3): 224–231.
  • 23. Delpierre C., Cuzin L., Fillaux J., Alvarez M., Massip P., Lang T. A systematic review of computer-based patient record systems and quality of care: more randomized clinical trials or a broader approach? Int. J. Qual Health Care 2004; 16(5): 407–416.
  • 24. Sintchenko V., Iredell J.R., Gilbert G.L., Coiera E. Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. J. Am. Med. Inform. Assoc. 2005; 12(4): 398–402.
  • 25. Durieux P., Nizard R., Ravaud P., Mounier N., Lepage E. A clinical decision support system for prevention of venous thromboembolism: effect on physician behavior. JAMA 2000; 283: 2816–2821.
  • 26. Leslie S.J., Denvir M.A. Clinical decision support software for chronic heart failure. Crit. Pathw. Cardiol. 2007; 6: 121–126.
  • 27. Dolan J.G. Shared decision-making – transferring research into practice: the Analytic Hierarchy Process (AHP). Patient. Educ. Couns. 2008; 73: 418–425.
  • 28. Hardy D., Smith B. Decision making in clinical practice. Brit. J. Anaest. Rec. Nurs. 2008; 9: 19–21.
  • 29. Wennberg J.E. Improving the medical decision-making process. Health. Aff. 1988; 7: 99–106.
  • 30. Bates D.W., Kuperman G.J., Wang S. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J. Am. Med. Inform. Assoc. 2006; 10: 523–530.
  • 31. Lin C., Lin CM., Lin B., Yang M.C. A decision support system for improving doctor’s prescribing behavior. Expert. Syst. Appl. 2009; 36: 7975–7984.
  • 32. Young S.A., Chaney E., Shoai R. et al. Information technology to support improved care for chronic illness. J. Gen. Inter. Med. 2007; 22: 425–430.
  • 33. Sutton C.D. Classification and regression trees, bagging and boosting. In: Handbook of Statistics. Ed. Pfefferman D. Elsevier New York. 2010, 303–327.
  • 34. Breiman L., Friedman J., Stone C.J., Olshen R.A. Classification and regression trees. Chapman and Hall/CR, New York 1993.
  • 35. Quinlan J.R. C4.5: Programs for machine learning. Morgan Kauffman, London 1993.
  • 36. Koronacki J., Ćwik J. Statystyczne systemy uczące się. Oficyna Wydawnicza Exit, Warszawa 2008.
  • 37. Rokach L., Maimon O. Data mining with decision trees. World Scientific Publishing Co. Pte. Ltd. Singapore 2008.
  • 38. Almuallim H., Kaneda S., Akiba Y. Development and applications of decision trees. Expert Systems 2002; 1: 53–77.
  • 39. Kotsiantis S.B. Supervised machine learning: a review of classification techniques. Informatica 2007; 31: 249–268.
  • 40. Krzyśko M., Wołyński W., Górecki T., Skorzybut M. Systemy uczące się. Wydawnictwo WNT, Warszawa 2009.
  • 41. Podgorelec V., Kokol P., Stiglic B., Rozman I. Decision trees: an over-view and their use in medicine. J. Med. Sys. 2002; 26: 445–463.
  • 42. Loh W.Y., Shih Y.S. Split selection methods for classification trees. Stat. Sin. 1997; 7: 815–840.
  • 43. Esposito F., Malerba D., Semeraro G. A comparative analysis of methods for pruning decision trees. Machine Learning 1997; 19: 476–491.
  • 44. Hothorn T., Hornik K., Zeileis A. Unbiased Recursive Partitioning: A Conditional Inference Framework. J. Comp. Graphical. Stat. 2006; 15: 651–674.
  • 45. Lucas P.J.F., Abu-Hanna A. Prognostic methods in medicine. Artif. Intell. Med. 1999; 15: 105–119.
  • 46. Camdeviren H.A., Yazici A.C., Akkus Z., Bugdayci R., Sungur M.A. Comparison of logistic regression model and classification tree: an application to postpartum depression data. Expert Sys. Appl. 2007; 32: 987– –994.
  • 47. Mello F.C., Valle Baastos L.G., Soares S.L.M. et al. Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross sectional study. BMC Public Health 2006; 6(43): 1–8.
  • 48. Zhang H., Holford T., Bracnek M.B. A tree-based method of analysis for prospective studies. Stat. Med. 1996; 15: 37–49.
  • 49. Banerjee M., George J., Song EY., Roy A., Hryniuk W. Tree-based model for breast cancer prognostication. J. Clin. Oncol, 2004; 22: 2567–2574.
  • 50. Freckleton R.P. Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error. Behav. Ecol. Sociobiol. 2011; 65: 91–101.
  • 51. Marshall R.J. The use of classification and regression trees in clinical epidemiology. J. Clin. Epid. 2001; 54: 603–609.
  • 52. Long W. A comparison of logistic regression to decision-tree induction in a medical domain. Comp Biomed. Res. 1993; 26: 74–97.
  • 53. Tu Y.U., Gunnel D., Gilthorpe M.S. Simpson’s paradox, lord’s paradox, and suppression effect are the same phenomenon – the reversal paradox. Emerg. Themes Epidemiol. 2008; 5(2): 1–9.
  • 54. Rücker G., Schumacher M. Simpson’s paradox visualized: the example of the rosiglitazone meta-analysis. BMC Med. Res. Methodol. 2008; 8: 1–8.
  • 55. Ameringer S., Serlin RC., Ward S. Simpson’s paradox and experimental research. Nurs. Res. 2009; 58: 123–127.
  • 56. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data. John Wiley and Sons, New York 2002.
  • 57. Scheffer J. Dealing with missing data. Res. Lett. Inf. Math. Scil. 2002; 3: 153–160.
  • 58. Schafer J.L., Graham J.W. Missing data: our view of the state of the art. Psychol. Methods 2002; 7: 147–177.
  • 59. Muller R., Möckel M. Logistic regression and CART in the analysis of multimarker studies. Clin. Chim. Acta 2008; 394: 1–6.
  • 60. Walesiak M., Gatnar E. Statystyczna analiza danych z wykorzystaniem programu R. Wydawnictwo Naukowe PWN, Warszawa 2009.
  • 61. Zhang H., Holford T., Bracnek M.B. A tree-based method of analysis for prospective studies. Stat. Med. 1996; 15: 37–49.
  • 62. Mello F.C., Valle Baastos L.G., Soares S.L.M. Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross sectional study. BMC Public Health 2006; 6(43): 1–8.
  • 63. Takashi O., Cook EF., Nakamura T., Saito J., Ikawa F., Fukui T. Risk stratification for in hospital mortality in spontaneous intracerrebral haemor-rhage: A classification and regression tree analysis. QJM 2006; 99: 743–750.
  • 64. Austin PC. A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality. Stat. Med. 2007; 26: 293–2957.
  • 65. Negassa A., Monrad E.S., Bang J.Y., Vankeepuram S.S. Tree-structured risk stratification of in-hospital mortality after percutaneous coronary intervention for acute myocardial infarction: a report from the New York State percutaneous coronary intervention database. Am. Heart J. 2007; 154: 321– –329.
  • 66. Misztal M. O zastosowaniu statystycznych metod rozpoznawania obrazów do wspomagania procesów podejmowania decyzji w diagnostyce medycznej. StatSoft Polska 2003. http://www.statsoft.pl/czytelnia/ badanianaukowe/d1biolmed/ obrazy.pdf
  • 67. Szołtysek J., Trzpiot G. Drzewa klasyfikacyjne w badaniu preferencji komunikacyjnych. W: Studia Ekonomiczne nr 97: Modelowanie preferencji a ryzyko ’12. Red. T. Trzaskalik. Zesz. Nauk. Wydziałowe Uniwersytetu Ekonomicznego w Katowicach, Katowice 2012; 213–230.
  • 68. Trzpiot G., Ganczarek A. Drzewa decyzyjne w statystycznej analizie decyzji na przykładzie wirtualnych łańcuchów dostaw. FOE 2012; 271: 57–70.
Document Type
article
Publication order reference
YADDA identifier
bwmeta1.element.psjd-5dd55d20-8714-4008-803a-fd35a2111bca
Identifiers
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.