About this Journal Submit a Manuscript Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 274193, 16 pages
http://dx.doi.org/10.1155/2013/274193
Research Article

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

1Department of Computer and Information Science, University of Macau, Macau, China
2Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada P7B 5E1

Received 27 June 2013; Accepted 3 August 2013

Academic Editor: Tai-hoon Kim

Copyright © 2013 Simon Fong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Berlin, M. Sorani, and I. Sim, “A taxonomic description of computer-based clinical decision support systems,” Journal of Biomedical Informatics, vol. 39, no. 6, pp. 656–667, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. O. S. Mohammed and R. Benlamri, “Building a diseases symptoms ontology for medical diagnosis: an integrative approach,” in Proceedings of the IEEE International Conference on Future Generation Communication Technology (FGCT '12), pp. 104–108, British Computer Society, London, UK, December 2012.
  3. J. Fiaidhi and S. Mohammed, “Adopting personal learning environments for sharing electronic healthcare records,” in Proceedings of the World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 4011–4016, 2010.
  4. S. Fong, Y. Hang, S. Mohammed, and J. Fiaidhi, “Stream-based biomedical classification algorithms for analyzing biosignals,” Journal of Information Processing Systems, vol. 7, no. 4, pp. 717–732, 2011.
  5. I. T. BjØrk and G. A. Hamilton, “Clinical decision making of nurses working in hospital settings,” Nursing Research and Practice, vol. 2011, Article ID 524918, 8 pages, 2011. View at Publisher · View at Google Scholar
  6. S. Fong and Y. Hang, “Enabling real-time business intelligence by stream data mining,” in New Fundamental Technologies in Data Mining, K. Funatsu, Ed., Intech, Vienna, Austria, 2011.
  7. Y. Hang and S. Fong, “An experimental comparison of decision trees in traditional data mining and data stream mining,” in Proceedings of the 6th International Conference on Advanced Information Management and Service (IMS '10), pp. 442–447, Seoul, Korea, December 2010. View at Scopus
  8. M. J. Yuan, “Watson and healthcare: how natural language processing and semantic search could revolutionize clinical decision support,” Tech. Rep., IBM Developer Works, 2011.
  9. J. Sun, D. Sow, J. Hu, and S. Ebadollahi, “A system for mining temporal physiological data streams for advanced prognostic decision support,” in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM '10), pp. 1061–1066, Sydney, Australia, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Zhang, S. Fong, J. Fiaidhi, and S. Mohammed, “Real-time clinical decision support system with data stream mining,” Journal of Biomedicine and Biotechnology, vol. 2012, Article ID 580186, 8 pages, 2012. View at Publisher · View at Google Scholar
  11. H.-C. Lin, “Real-time clinical decision support system,” in Medical Informatics, pp. 111–136, InTech, 2012.
  12. S. Fong, S. Mohammed, J. Fiaidhi, and C. K. Kwoh, “Using causality modeling and Fuzzy Lattice Reasoning algorithm for predicting blood glucose,” Expert Systems With Applications, vol. 40, no. 18, pp. 7354–7366, 2013.
  13. V. Patkar, D. Acosta, T. Davidson, A. Jones, J. Fox, and M. Keshtgar, “Cancer multidisciplinary team meetings: evidence, challenges, and the role of clinical decision support technology,” International Journal of Breast Cancer, vol. 2011, Article ID 831605, 7 pages, 2011. View at Publisher · View at Google Scholar
  14. J. Szkoa, K. Pancerz, and J. Warcho, “Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study,” Computational Intelligence and Neuroscience, vol. 2011, Article ID 289398, 8 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Walsh, P. Cunningham, S. J. Rothenberg, S. O'Doherty, H. Hoey, and R. Healy, “An artificial neural network ensemble to predict disposition and length of stay in children presenting with bronchiolitis,” European Journal of Emergency Medicine, vol. 11, no. 5, pp. 259–264, 2004. View at Scopus
  16. L. B. Gerald, S. Tang, F. Bruce et al., “A decision tree for tuberculosis contact investigation,” American Journal of Respiratory and Critical Care Medicine, vol. 166, no. 8, pp. 1122–1127, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. B. G. Buchanan and E. H. Shortliffe, Rule Based Expert Systems: the MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, Reading, MA, USA, 1984.
  18. Skepticsm about MYCIN Method, June 2013, http://en.wikipedia.org/wiki/Mycin#Method.
  19. H. R. Warner Jr. and O. Bouhaddou, “Innovation review: iliad—a medical diagnostic support program,” Topics in Health Information Management, vol. 14, no. 4, pp. 51–58, 1994. View at Scopus
  20. A. Bar-Or, D. Goddeau, J. Healey, L. Kontothanassis, and B. Logan, “BioStream: a system architecture for real-time processing of physiological signals,” in Proceedings of the IEEE Engineering in Medicine and Biology Society Conference (EMBS '04), pp. 3101–3104, San Francisco, Calif, USA, September 2004.
  21. D. J. Abadi, D. Carney, U. Çetintemel et al., “Aurora: a new model and architecture for data stream management,” The VLDB Journal, vol. 12, no. 2, pp. 120–139, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. B. P. Kovatchev, “Diabetes technology: markers, monitoring, assessment, and control of blood glucose fluctuations in diabetes,” Scientifica, vol. 2012, Article ID 283821, 14 pages, 2012. View at Publisher · View at Google Scholar
  23. D. Takahashi, Y. Xiao, and F. Hu, “A survey of insulin-dependent diabetes—part II: control methods,” International Journal of Telemedicine and Applications, vol. 2008, Article ID 739385, 14 pages, 2008. View at Publisher · View at Google Scholar
  24. H. Yang and S. Fong, “Incremental optimization mechanism for constructing a decision tree in data stream mining,” Mathematical Problems in Engineering, vol. 2013, Article ID 580397, 14 pages, 2013. View at Publisher · View at Google Scholar
  25. T. Anwar, S. Asghar, and S. Fong, “Bayesian based subgroup discovery,” in Proceedings of the 6th International Conference on Digital Information Management (ICDIM '11), pp. 154–161, Melbourne, Australia, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. G. Robertson, E. D. Lehmann, W. Sandham, and D. Hamilton, “Blood glucose prediction using artificial neural networks trained with the AIDA diabetes simulator: a proof-of-concept pilot study,” Journal of Electrical and Computer Engineering, vol. 2011, Article ID 681786, 11 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. A. J. Viera and J. M. Garrett, “Understanding interobserver agreement: the kappa statistic,” Family Medicine, vol. 37, no. 5, pp. 360–363, 2005. View at Scopus
  28. S. Fong and A. Cerone, “Attribute overlap minimization and outlier elimination as dimensionality reduction techniques for text classification algorithms,” Journal of Emerging Technologies in Web Intelligence, vol. 4, no. 3, pp. 259–263, 2012.
  29. H. Yang, S. Fong, R. Wong, and G. Sun, “Optimizing classification decision trees by using weighted naïve bayes predictors to reduce the imbalanced class problem in wireless sensor network,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 460641, 15 pages, 2013. View at Publisher · View at Google Scholar