Predictive Models for Hospital Bed Management Using Data Mining Techniques

  • Sérgio OliveiraEmail author
  • Filipe Portela
  • Manuel F. Santos
  • José Machado
  • António Abelha
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 276)


It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient’s management and providing important information for managers to support their decisions.

Throughout this study, were induced DM models capable to make predictions in a real environment using real data. For this, was adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Three distinct techniques were considered: Decision Trees (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) to perform classification tasks. This work explored the possibility to predict the number of patient discharges using only the number of discharges veirifed in the past. The models developed are able to predict the number of patient discharges per week with acuity values ranging from ≈82.69% to ≈94.23%. The use of these models can improve the efficiency of the administration of hospital beds. An accurate forecasting of discharges allows a better estimate of the beds available for the coming weeks.


Hospital Management Management of Patients Management of Beds and Data Mining 


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  1. 1.
    Santos, M., Azevedo, C.: Data Mining Descoberta do conhecimento em base de dados. FCA - Editora de Informática, Lda (2005)Google Scholar
  2. 2.
    Santos, M., Boa, M., Portela, F., Silva, Á., Rua, F.: Real-time prediction of organ failure and outcome in intensive medicine. In: 2010 5th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2010)Google Scholar
  3. 3.
    Koh, H., Tan, G.: Data mining applications in healthcare. J. Healthc Inf. Manag. 19(2), 64–72 (2005)Google Scholar
  4. 4.
    WHO, Expert Committee on Health Statistics, 261 (1963) Google Scholar
  5. 5.
    Santos, I., Arruda, J.: Análise do Perfil Profissional dos Gestores dos Hospitais Particulares da Cidade de Aracaju- SE. Revista Eletronica da Faculdade José Augusto Vieira Na-77 (2012)Google Scholar
  6. 6.
    Proença, J., Vaz, A., Escoval, A., Candoso, F., Ferro, D., Carapeto, C., Costa, R., Roeslin, V.: O Hospital Português. Vida Económica-Conferforum (2000)Google Scholar
  7. 7.
    Neves, M.: Os Médicos vão ter de ser os motores da reforma do sistema. Revista Portuguesa de Gestão & Saúde (5) (2011)Google Scholar
  8. 8.
    Yang, G., Sun, L., Lin, X.: Six-stage Hospital Beds Arrangement Management System. presented at the Management and Service Science (2010)Google Scholar
  9. 9.
    Dwivedi, A., Bali, R., Naguib, R.: Building New Healthcare Management Paradigms: A Case for Healthcare Knowledge Management. presented at the Healthcare Knowledge Management Issues, Advances, and Successes (2006)Google Scholar
  10. 10.
    Bose, R.: Knowledge management-enabled health care management systems: capabilities, infrastructure, and decision-support. Expert Systems with Applications 24(1), 59–71 (2003)CrossRefGoogle Scholar
  11. 11.
    Tsumoto, S., Hirano, S.: Data mining in hospital information system for hospital management. presented at the ICME International Conference on Complex Medical Engineering, CME 2009, pp. 1–5 (2009)Google Scholar
  12. 12.
    Tsumoto, S., Hirano, S.: Towards Data-Oriented Hospital Services: Data Mining-based Hospital Mangement. In: presented at the The 10th IEEE International Conference on Data Mining Workshops, Sydney, Australia (2011)Google Scholar
  13. 13.
    Teow, K., Darzi, E., Foo, E., Jin, X., Sim, J.: Intelligent Analysis of Acute Bed Overflow in a Tertiary Hospital in Singapore. Springer US (2012)Google Scholar
  14. 14.
    Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems, 9a Edição. Prentice Hall (2011)Google Scholar
  15. 15.
    Maimon, O., Rokach, L.: Introduction to Knowledge Discovery and Data Mining. In: Data Mining and Knowledge Discovery Handbook, 2a Edição. Springer (2010)Google Scholar
  16. 16.
    Torgo, L.: Data Mining with R: Learning with Case Studies. CRC Press - Taylor & Francis Group (2011)Google Scholar
  17. 17.
    Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: Misc Functions of the Department of Statistics (e1071) (2012)Google Scholar
  18. 18.
    Cortez, P.: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression (2013)Google Scholar
  19. 19.
    Reis, E.: Estatística Descritiva, 7a ed. Edição Sílabo (2008)Google Scholar
  20. 20.
    Witten, I., Frank, E., Hall, M.: Data Mining Pratical Machine Learning Tools and Techniques, 3a Edição. Morgan Kaufmann (2011)Google Scholar
  21. 21.
    Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation, Encyclopedia of Database Systems, vol. 5. Springer (2009)Google Scholar
  22. 22.
    Kantardzic, M.: Data Mining Concepts, Models, Methods, and Algorithms, 2a Edição. Wiley - IEEE Press (2011)Google Scholar
  23. 23.
    Ben-Hur, A., Weston, J.: A User’s Guide to Support Vector Machines. In: Carugo, O., Eisenhaber, F. (eds.) Data Mining Techniques for the Life Sciences. Humana Press (2010)Google Scholar
  24. 24.
    Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3a Edição. Morgan Kaufmann (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sérgio Oliveira
    • 1
    Email author
  • Filipe Portela
    • 1
  • Manuel F. Santos
    • 1
  • José Machado
    • 2
  • António Abelha
    • 2
  1. 1.Algoritmi CentreUniversity of MinhoGuimarãesPortugal
  2. 2.CCTCUniversity of MinhoBragaPortugal

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