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Case-Based Reasoning Applied to Medical Diagnosis and Treatment

  • Xiomara BlancoEmail author
  • Sara Rodríguez
  • Juan M. Corchado
  • Carolina Zato
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

The Case-Based Reasoning (CBR) is an appropriate methodology to apply in diagnosis and treatment. Research in CBR is growing and there are shortcomings, especially in the adaptation mechanism. In this paper, besides presenting a methodological review of the technology applied to the diagnostics and health sector published in recent years, a new proposal is presented to improve the adaptation stage. This proposal is focused on preparing the data to create association rules that help to reduce the number of cases and facilitate learning adaptation rules.

Keywords

Association Rule Medical Diagnosis Adaptive Resonance Theory Adaptation Rule Fuzzy Decision Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chuang, C.L.: Case-based reasoning support for liver disease diagnosis. Artif. Intell. Med. 53(1), 15–23 (2011)CrossRefGoogle Scholar
  2. 2.
    Petrovic, S., Mishra, N., Sundar, S.: A novel case based reasoning approach to radiotherapy planning. Expert Systems with Applications 38(9), 10759–10769 (2011)CrossRefGoogle Scholar
  3. 3.
    Ocampo, E., MacEiras, M., Herrera, S., Maurente, C., Rodríguez, D., Sicilia, M.A.: Comparing Bayesian inference and case-based reasoning as support techniques in the diagnosis of Acute Bacterial Meningitis. Expert Systems with Applications 38(8), 10343–10354 (2011)CrossRefGoogle Scholar
  4. 4.
    Ting, S.L., Kwok, S.K., Tsang, A.H.C., Lee, W.B.: A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center. Knowledge-Based Systems 24(3), 444–456 (2011)CrossRefGoogle Scholar
  5. 5.
    Ahmed, M.U., Begum, S., Funk, P., Xiong, N., von Scheele, B.: A multi-module case-based biofeedback system for stress treatment. Artificial Intelligence in Medicine 51(2), 107–115 (2011)CrossRefGoogle Scholar
  6. 6.
    Hsu, K.H., Chiu, C., Chiu, N.H., Lee, P.C., Chiu, W.K., Liu, T.H., et al.: A case-based classifier for hypertension detection. Knowledge-Based Systems 24(1), 33–39 (2011)CrossRefGoogle Scholar
  7. 7.
    Agwil, R.O., Shrivastava, D.P.: Integrated Thallassaemia Decision Support System. WSEAS Transactions on Computers 9(8), 857–867 (2010)Google Scholar
  8. 8.
    Huang, M.L., Hung, Y.H., Lee, W.M., Li, R.K., Wang, T.H.: Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis. Journal of Medical Systems (in press)Google Scholar
  9. 9.
    Gu, D.X., Liang, C.Y., Li, X.G., Yang, S.L., Zhang, P.: Intelligent technique for knowledge reuse of dental medical records based on case-based reasoning. Journal of Medical Systems 34(2), 213–222 (2010)CrossRefGoogle Scholar
  10. 10.
    Marling, C., Shubrook, J., Schwartz, F.: Toward case-based reasoning for diabetes management: A preliminary clinical study and decision support system prototype. Computational Intelligence 25(3), 165–179 (2009)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Toward translational incremental similarity-based reasoning in breast cancer grading. Image Perception, Access and Language IPAL (UMI CNRS 2955, UJF, NUS, I2R), Singapore National University Hospital National University of Singapore Politehnica University of Timisoara, Romania University of Besançon, France Medical Informatics Service, University Hospital of Geneva, Sweden University of Applied Sciences, Western Switzerland, Sierre, Sweden (2009)Google Scholar
  12. 12.
    Rodríguez, S., De Paz, J.F., Bajo, J., Corchado, J.M.: Applying CBR systems to micro array data classification 49, 102–111 (2009)Google Scholar
  13. 13.
    Ahn, H., Kim, K.: Global optimization of case-based reasoning for breast cytology diagnosis. Expert Systems with Applications 36(1), 724–734 (2009)CrossRefGoogle Scholar
  14. 14.
    Obot, O.U., Uzoka, F.M.: A framework for application of neuro-case-rule base hybridization in medical diagnosis. Applied Soft Computing 9(1), 245–253 (2009)CrossRefGoogle Scholar
  15. 15.
    Fazel Zarandi, M.H., Zarinbal, M., Izadi, M.: Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach. Applied Soft Computing 11(1), 285–294 (2011)CrossRefGoogle Scholar
  16. 16.
    Cruz-Ramírez, N., Acosta-Mesa, H.G., Carrillo-Calvet, H., Barrientos-Martínez, R.E.: Discovering interobserver variability in the cytodiagnosis of breast cancer using decision trees and Bayesian networks. Applied Soft Computing 9(4), 1331–1342 (2009)CrossRefGoogle Scholar
  17. 17.
    A Reuse-Based CBR System Evaluation in Critical Medical Scenarios (2009)Google Scholar
  18. 18.
    Fan, C.-Y., Chang, P.-C., Lin, J.-J., Hsieh, J.C.: A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Applied Soft Computing 11(11), 632–644 (2009)Google Scholar
  19. 19.
    Lin, R.H., Chuang, C.L.: A hybrid diagnosis model for determining the types of the liver disease. Computers in Biology and Medicine 40(7), 665–670 (2010)CrossRefGoogle Scholar
  20. 20.
    Influenza Forecast: Case-Based Reasoning or Statistics? (2007)Google Scholar
  21. 21.
    Floyd Jr., C.E., Lo, J.Y., Tourassi, G.D.: Case-Based Reasoning Computer Algorithm that Uses Mammographic Findings for Breast Biopsy Decisions. American Journal of Roentgenology 175(5), 1347–1352 (2000)CrossRefGoogle Scholar
  22. 22.
    Schmidt, R., Vorobieva, O., Gierl, L.: Case-based Adaptation Problems in Medicine, pp. 267–274 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Xiomara Blanco
    • 1
    Email author
  • Sara Rodríguez
    • 1
  • Juan M. Corchado
    • 1
  • Carolina Zato
    • 1
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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