ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing

Manuel Santos, Filipe Portela


Ubiquitous Data Mining and Intelligent Decision Support Systems are gaining interest by both computer science researchers and intensive care doctors. Previous work contributed with Data Mining models to predict organ failure and outcome of patients in order to support and guide the clinical decision based on the notion of critical events and the data collected from monitors in real-time. This paper addresses the study of the impact of the Modified Early Warning Score, a simple physiological score that may allow improvements in the quality and safety of management provided to surgical ward patients, in the prediction sensibility. The feature selection and data pre-processing are also detailed. Results show that for some variables associated to this score the impact is minimal.


  1. Bergs, E. A. G., Rutten, F. L. P. A., Tadros, T., Krijnen, P., & Schipper, I. B. (2005). Communication during trauma resuscitation: do we know what is happening? Injury, 36(8), 905-911.
  2. Donchin, Y., & Seagull, F. J. (2002). The hostile environment of the intensive care unit. Current opinion in critical care, 8(4), 316.
  3. Fano, A., & Gershman, A. (2002). The future of business services in the age of ubiquitous computing. Communications of the ACM, 45(12), 87.
  4. Gardner-Thorpe, J., Love, N., Wrightson, J., Walsh, S., & Keeling, N. (2006). The value of Modified Early Warning Score (MEWS) in surgical in-patients: a prospective observational study. Annals of The Royal College of Surgeons of England, 88(6), 571.
  5. Giudici, P. (2003). Applied data mining: Statistical methods for business and industry: John Wiley & Sons, Inc.
  6. Goñi, A., Burgos, A., Dranca, L., Rodríguez, J., Illarramendi, A., & Bermúdez, J. (2009). Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor datastreams. Computer Methods and Programs in Biomedicine, 96(2), 141-157.
  7. Horovitz, O., Gaber, M. M., & Krishnaswamy, S. (2005). Making sense of ubiquitous data streams - A fuzzy logic approach. In R. Khosla, R. J. Howlett & L. C. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems, Pt 2, Proceedings (Vol. 3682, pp. 922-928). Berlin: Springer-Verlag Berlin.
  8. Hsu, J. (2002, 2002). Data mining trends and developments: The key data mining technologies and applications for the 21st century.
  9. Kargupta, H. (2001, 2001). CAREER: Ubiquitous Distributed Knowledge Discovery from Heterogeneous Data.
  10. Kohn, L. T., Corrigan, J., & Donaldson, M. S. (2000). To Err Is Human: Building a Safer Health System: National Academy Press.
  11. Langheinrich, M. (2001). Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems. Paper presented at the Proceedings of UbiComp 2001.
  12. Menachemi, N., & Brooks, R. G. (2006). Reviewing the benefits and costs of electronic health records and associated patient safety technologies. Journal of Medical Systems, 30(3), 159-168.
  13. Neaga, E. I., & Harding, J. A. (2005). An enterprise modeling and integration framework based on knowledge discovery and data mining. International Journal of Production Research, 43(6), 1089-1108.
  14. Nigel, D. (2002). Beyond Prototypes: Challenges in Deploying Ubiquitous Systems, 1, 26-35.
  15. Orwat, C., Graefe, A., & Faulwasser, T. (2008). Towards pervasive computing in health care-A literature review. BMC Medical Informatics and Decision Making, 8(1), 26.
  16. Piramuthu, S. (2003). On learning to predict web traffic. Decision Support Systems, 35(2), 213-229.
  17. Saha, D., & Mukherjee, A. (2003). Pervasive computing: a paradigm for the 21st century. Computer, 25-31.
  18. Santos, M. F., Portela, F., Vilas-Boas, M., Machado, J., Abelha, A., Neves, J., et al. (2009). Information Modeling for Real-Time Decision Support in Intensive Medicine. In S. Y. Chen & Q. Li (Eds.), Proceedings of the 8th Wseas International Conference on Applied Computer and Applied Computational Science - Applied Computer and Applied Computational Science (pp. 360-365). Athens: World Scientific and Engineering Acad and Soc.
  19. Scicluna, P., Murray, A., Xiao, Y., & Mackenzie, C. F. (2008). Challenges to Real-Time Decision Support in Health Care. Agency for Healthcare Research and Quality.
  20. Silva, Á., Cortez, P., Santos, M. F., Gomes, L., & Neves, J. (2008). Rating organ failure via adverse events using data mining in the intensive care unit. Arificial Intelligence in Medicine, 43(3), 179-193.
  21. Varshney, U. (2007). Pervasive healthcare and wireless health monitoring. Mobile Networks and Applications, 12(2), 113-127.
  22. Varshney, U. (2009). Pervasive Healthcare Computing: EMR/EHR, Wireless and Health Monitoring: SpringerVerlag New York Inc.
  23. Vilas-Boas, M., Santos, M. F., Portela, F., Silva, Á., & Rua, F. (2010). Hourly prediction of organ failure and outcome in intensive care based on data mining techniques. Paper presented at the 12th International Conference on Enterprise Information Systems.
  24. Villas Boas, M., Gago, P., Portela, F., Rua, F., Silva, Á., & Santos, M. F. (2010). Distributed and real time Data Mining in the Intensive Care Unit. Paper presented at the 19th European Conference on Artificial Intelligence - ECAI 2010.
  25. Vincent, J. L., de Mendonca, A., Cantraine, F., Moreno, R., Takala, J., Suter, P. M., et al. (1998). Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Critical care medicine, 26(11), 1793.
  26. Vincent, J. L., Moreno, R., Takala, J., Willatts, S., De Mendonca, A., Bruining, H., et al. (1996). The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive care medicine, 22(7), 707-710.

Paper Citation

in Harvard Style

Santos M. and Portela F. (2011). ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-53-9, pages 261-266. DOI: 10.5220/0003507302610266

in Bibtex Style

author={Manuel Santos and Filipe Portela},
title={ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing
SN - 978-989-8425-53-9
AU - Santos M.
AU - Portela F.
PY - 2011
SP - 261
EP - 266
DO - 10.5220/0003507302610266