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A Case-Based Reasoning Framework for Clinical Decision Making

  • Ivett E. Fuentes Herrera
  • Beatriz Valdés Pérez
  • María M. García Lorenzo
  • Leticia Arco García
  • Mabel M. Herrera González
  • Rolando de la C. Fuentes Morales
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)

Abstract

The information is increasing in hospital centers due to the widespread use of Electronical Medical Records, which make it necessary to develop new methods capable of processing information and ensuring its productive use. In this paper is proposed a framework of case-based reasoning for systems of clinical decision making by using the complete linkage hierarchical algorithm. Next, it is shown the series of steps that involve the pre-processing performed in this work and how to calculate a similarity measure between the new problem and each case based on textual features. The presented retrieve and adaptation mechanisms allow a better case retrieval and they can support clinical diagnosis.

Keywords

Clustering Knowledge discovering Case base reasoning Electronic medical records 

Notes

Acknowledgements

The authors acknowledge the support of the Department of Propaedeutics and Internal Medicine of the Provincial Hospital “Celestino Hernández Robau” in Villa Clara, Cuba. We are especially grateful to the doctors of the Internal Medicine Service.

References

  1. 1.
    Gunter, T.D., Terry, N.P.: The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. J. Med. Internet Res. 7(1), 3 (2005)CrossRefGoogle Scholar
  2. 2.
    Fiks, A.G., et al.: Electronic medical record use in pediatric primary care. J. Am. Med. Inform. Assoc. 18(1), 38–44 (2010)CrossRefGoogle Scholar
  3. 3.
    Fernández, A., et al.: Analysis of health professional security behaviors in a real clinical setting: an empirical study. Int. J. Med. Inf. 84(6), 454–467 (2015)CrossRefGoogle Scholar
  4. 4.
    Martínez, I.G., Pérez, R.E.B.: Making decision in case-based systems using probabilities and rough sets. Knowl.-Based Syst. 16(4), 205–213 (2003)CrossRefGoogle Scholar
  5. 5.
    Lorenzo, M.M.G., Pérez, R.E.B.: A model and its different applications to case-based reasoning. Knowl.-Based Syst. 9(7), 465–473 (1996)CrossRefGoogle Scholar
  6. 6.
    De Mantaras, R.L., et al.: Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 20(3), 215–240 (2005)CrossRefGoogle Scholar
  7. 7.
    Kriegsman, M., Barletta, R.: Building a case-based help desk application. IEEE Expert 8(6), 18–26 (1993)CrossRefGoogle Scholar
  8. 8.
    Simoudis, E.: Using case-based retrieval for customer technical support. IEEE Expert 7(5), 7–12 (1992)CrossRefGoogle Scholar
  9. 9.
    Magdaleno, D., et al.: Clustering XML documents using structure and content based on a new similarity function OverallSimSUX. Comput. y Sist. 19(1), 151–161 (2015)Google Scholar
  10. 10.
    Fuentes, I.E., et al.: Methodology for discovery of implicit knowledge in medical records. In: Paper Presented at the Fifth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support, EUREKA 2015, Universidad Autónoma Metropolitana. Ciudad México (2015)Google Scholar
  11. 11.
    Magdaleno, D.G., et al.: Comparative study of clustering algorithms using OverallSimSUX similarity function for XML documents. Intel. Artif.: Rev. Iberoam. Intel. Artif. 18(55), 69–80 (2015)CrossRefGoogle Scholar
  12. 12.
    Fuentes, I.E., et al.: Toma de decisiones inteligente a partir de registros médicos almacenados en CDA-HL7. Rev. Cuba. Informática Médica 8(1), 109–124 (2016)MathSciNetGoogle Scholar
  13. 13.
    Fuentes, I.E., et al.: Metodología para asistir la toma de decisiones diagnóstica a partir del descubrimiento del conocimiento implícito en Historias Clínicas. Rev. Cienc. Matemáticas 29(2), 99–106 (2015)MathSciNetGoogle Scholar
  14. 14.
    Salton, G., Wong, A., Yang, C.-S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefGoogle Scholar
  15. 15.
    Frakes, W., Baeza-Yates, R.: Information Retrieval. Data Structure and Algorithms. Prentice Hall, New York (1992)Google Scholar
  16. 16.
    Fernández, A., et al.: Sentiment analysis and topic detection of Spanish tweets: a comparative study of NLP techniques. Proces. Del Leng. Nat. 50, 45–52 (2013)Google Scholar
  17. 17.
    Amores, M., et al.: Efectos de la Negación, Modificadores, Jergas, Abreviaturas y Emoticonos en el Análisis de Sentimiento. In: Proceedings of the 2nd International Workshop on Semantic Web (IWSW). CEUR, La Habana (2016)Google Scholar
  18. 18.
    Zafra, J., et al.: Tratamiento de la Negación en el Análisis de Opiniones en Español. Proces. Del Leng. Nat. 54, 37–44 (2015)Google Scholar
  19. 19.
    Beers, M.H., et al.: The Merck Manual of Medical Information. Pocket Books, New York (2003)Google Scholar
  20. 20.
    Manning, C.D., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  21. 21.
    Jurgens, D., Stevens, K.: The S-Space package: an open source package for word space models. In: Proceedings of the ACL 2010 System Demonstrations. Association for Computational Linguistics, pp. 30–35 (2010)Google Scholar
  22. 22.
    Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, Berlin (2012).  https://doi.org/10.1007/978-1-4614-3223-4CrossRefGoogle Scholar
  23. 23.
    Steinbach, M., et al.: A comparison of document clustering techniques. In: Proceedings of 6th ACM SIGKDD World Text Mining Conference, Boston. ACM Press (2000)Google Scholar
  24. 24.
    Wilcoxon, F.: Individual comparisons byranking methods. Biom. Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar
  25. 25.
    Cawley, G.C.: Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In: Paper Presented at the Neural Networks 2006. IJCNN 2006 (2006)Google Scholar
  26. 26.
    Wess, S., Althoff, K.-D., Derwand, G.: Using k-d trees to improve the retrieval step in case-based reasoning. In: Wess, S., Althoff, K.-D., Richter, Michael M. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 167–181. Springer, Heidelberg (1994).  https://doi.org/10.1007/3-540-58330-0_85CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ivett E. Fuentes Herrera
    • 1
  • Beatriz Valdés Pérez
    • 1
  • María M. García Lorenzo
    • 1
  • Leticia Arco García
    • 1
  • Mabel M. Herrera González
    • 2
  • Rolando de la C. Fuentes Morales
    • 2
  1. 1.Computer Science DepartmentUniversidad Central “Marta Abreu” de Las VillasSanta ClaraCuba
  2. 2.Medicine DepartmentUniversidad de Ciencias Médicas de Villa Clara Dr. “Serafín Ruíz de Zarate Ruiz”Santa ClaraCuba

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