An Integrated Approach for Healthcare Planning over Multi-dimensional Data Using Long-Term Prediction

  • Rui Henriques
  • Cláudia Antunes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7231)

Abstract

The mining of temporal aspects over multi-dimensional data is increasingly critical for healthcare planning tasks. A healthcare planning task is, in essence, a classification problem over health-related attributes across temporal horizons. The increasingly integration of healthcare data through multi-dimensional structures triggers new opportunities for an adequate long-term planning of resources within and among clinical, pharmaceutical, laboratorial, insurance and e-health providers. However, the flexible nature and random occurrence of health records claim for the ability to deal with both structural attribute-multiplicity and arbitrarily-high temporal sparsity. For this purpose, two solutions using different structural mappings are proposed: an adapted multi-label classifier over denormalized tabular data and an adapted multiple time-point classifier over multivariate sparse time sequences. This work motivates the problem of long-term prediction in healthcare, and places key requirements and principles for its accurate and efficient solution.

Keywords

Health Record Multivariate Adaptive Regression Spline Inductive Logic Programming Normalize Root Mean Square Error Time Series Prediction 
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.
    Antunes, C.: Pattern Mining over Nominal Event Sequences using Constraint Relaxations. Ph.D. thesis, Instituto Superior Tecnico (2005)Google Scholar
  2. 2.
    Antunes, C.: Temporal pattern mining using a time ontology. In: EPIA, pp. 23–34. Associação Portuguesa para a Inteligência Artificial (2007)Google Scholar
  3. 3.
    Antunes, C.: An ontology-based framework for mining patterns in the presence of background knowledge. In: ICAI, pp. 163–168. PTP, Beijing (2008)Google Scholar
  4. 4.
    Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order markov models. J. Artif. Int. Res. 22, 385–421 (2004)MathSciNetMATHGoogle Scholar
  5. 5.
    Bellazzi, R., Ferrazzi, F., Sacchi, L.: Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 1(5), 416–430 (2011)CrossRefGoogle Scholar
  6. 6.
    Ben Taieb, S., Sorjamaa, A., Bontempi, G.: Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomput. 73, 1950–1957 (2010)CrossRefGoogle Scholar
  7. 7.
    Bengio, S., Fessant, F., Collobert, D.: Use of modular architectures for time series prediction. Neural Process. Lett. 3, 101–106 (1996)CrossRefGoogle Scholar
  8. 8.
    Berthold, M., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction. Springer-Verlag New York, Inc., Secaucus (1999)MATHGoogle Scholar
  9. 9.
    Bontempi, G., Ben Taieb, S.: Conditionally dependent strategies for multiple-step-ahead prediction in local learning. Int. J. of Forecasting 27(2004), 689–699 (2011)CrossRefGoogle Scholar
  10. 10.
    Brahim-Belhouari, S., Bermak, A.: Gaussian process for nonstationary time series prediction. Computational Statistics and Data Analysis 47(4), 705–712 (2004)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)MATHGoogle Scholar
  12. 12.
    Brown, P.J., Vannucci, M., Fearn, T.: Multivariate bayesian variable selection and prediction. Journal of the Royal Statistical Society 60(3), 627–641 (1998)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)CrossRefGoogle Scholar
  14. 14.
    Carrasco, R.C., Oncina, J.: Learning Stochastic Regular Grammars by Means of a State Merging Method. In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 139–152. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  15. 15.
    Cheng, H., Tan, P.-N., Gao, J., Scripps, J.: Multistep-Ahead Time Series Prediction. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 765–774. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Cortez, P., Rocha, M., Neves, J.: A Meta-Genetic Algorithm for Time Series Forecasting. In: Proc. of AIFTSA 2001, EPIA 2001, Porto, Portugal, pp. 21–31 (2001)Google Scholar
  17. 17.
    Cotofrei, P., Neuchâtel, U.: Rule extraction from time series databases using classification trees. In: Proc. of the 20th IASTED, pp. 327–332. ACTA Press (2002)Google Scholar
  18. 18.
    Dietterich, T.G., Michalski, R.S.: Discovering patterns in sequences of events. Artif. Intell. 25, 187–232 (1985)CrossRefGoogle Scholar
  19. 19.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.J.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment 1(2), 1542–1552 (2008)Google Scholar
  20. 20.
    Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: 5th ACM SIGKDD, KDD, pp. 43–52. ACM, NY (1999)Google Scholar
  21. 21.
    Eddy, S.R.: Profile hidden markov models. Bioinformatics/Computer Applications in the Biosciences 14, 755–763 (1998)CrossRefGoogle Scholar
  22. 22.
    Fang, Y., Koreisha, S.G.: Updating arma predictions for temporal aggregates. Journal of Forecasting 23(4), 275–296 (2004)CrossRefGoogle Scholar
  23. 23.
    Freksa, C.: Temporal reasoning based on semi-intervals. A. Int. 54, 199–227 (1992)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Guimarães, G.: The Induction of Temporal Grammatical Rules from Multivariate Time Series. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 127–140. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  25. 25.
    Guyet, T., Garbay, C., Dojat, M.: Knowledge construction from time series data using a collaborative exploration system. J. of Biomedical Inf. 40, 672–687 (2007)CrossRefGoogle Scholar
  26. 26.
    Hsu, C.N., Chung, H.H., Huang, H.S.: Mining skewed and sparse transaction data for personalized shopping recommendation. Mach. Learn. 57, 35–59 (2004)CrossRefGoogle Scholar
  27. 27.
    Ji, Y., Hao, J., Reyhani, N., Lendasse, A.: Direct and Recursive Prediction of Time Series Using Mutual Information Selection. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 1010–1017. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  28. 28.
    Kersting, K., De Raedt, L., Gutmann, B., Karwath, A., Landwehr, N.: Relational Sequence Learning. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic ILP 2007. LNCS (LNAI), vol. 4911, pp. 28–55. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. John Wiley & Sons, Inc., NY (2002)Google Scholar
  30. 30.
    Kleinfeld, D., Sompolinsky, H.: Associative neural network model for the generation of temporal patterns: Theory and application to central pattern generators. Biophysical Journal 54(6), 1039–1051 (1988)CrossRefGoogle Scholar
  31. 31.
    Koch, I., Naito, K.: Prediction of multivariate responses with a selected number of principal components. Comput. Stat. Data Anal. 54, 1791–1807 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)MATHGoogle Scholar
  33. 33.
    Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana-academy Proceedings in Engineering Sciences 31, 173–198 (2006)MathSciNetMATHGoogle Scholar
  34. 34.
    Laxman, S., Sastry, P.S., Unnikrishnan, K.P.: Discovering frequent episodes and learning hidden markov models: A formal connection. IEEE Trans. on Knowl. and Data Eng. 17, 1505–1517 (2005)CrossRefGoogle Scholar
  35. 35.
    Lee, T.S., Chiu, C.C., Chou, Y.C., Lu, C.J.: Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis 50(4), 1113–1130 (2006)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Lesh, N., Zaki, M.J., Ogihara, M.: Mining features for sequence classification. In: Proc. of the 5th ACM SIGKDD, pp. 342–346. ACM, NY (1999)Google Scholar
  37. 37.
    Liu, J., Yuan, L., Ye, J.: An efficient algorithm for a class of fused lasso problems. In: Proc. of the 16th ACM SIGKDD, KDD, pp. 323–332. ACM, NY (2010)Google Scholar
  38. 38.
    Mannila, H., Toivonen, H., Inkeri Verkamo, A.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1, 259–289 (1997)CrossRefGoogle Scholar
  39. 39.
    Marcellino, M., Stock, J.H., Watson, M.W.: A comparison of direct and iterated multistep ar methods for forecasting macroeconomic time series. Journal of Econometrics 135(1-2), 499–526 (2006)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Mörchen, F.: Time series knowledge mining. W. in Dissertationen, G&W (2006)Google Scholar
  41. 41.
    Mörchen, F.: Tutorial cidm-t temporal pattern mining in symbolic time point and time interval data. In: CIDM. IEEE (2009)Google Scholar
  42. 42.
    Quinlan, J.R.: Learning with continuous Classes. In: 5th Australian Joint Conf. on Artificial Intelligence, pp. 343–348 (1992)Google Scholar
  43. 43.
    Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowl. and Data Eng. 8, 970–974 (1996)CrossRefGoogle Scholar
  44. 44.
    Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomput. 70, 2861–2869 (2007)CrossRefGoogle Scholar
  45. 45.
    Sorjamaa, A., Lendasse, A.: Time series prediction using dirrec strategy. In: ESANN, pp. 143–148 (2006)Google Scholar
  46. 46.
    Sun, R., Giles, C.L.: Sequence learning: From recognition and prediction to sequential decision making. IEEE Intelligent Systems 16, 67–70 (2001)CrossRefGoogle Scholar
  47. 47.
    Sun, R., Peterson, T.: Autonomous learning of sequential tasks: experiments and analyses. IEEE Transactions on Neural Networks 9(6), 1217–1234 (1998)CrossRefGoogle Scholar
  48. 48.
    Sutton, R.S.: Learning to predict by the methods of temporal differences. Machine Learning 3, 9–44 (1988)Google Scholar
  49. 49.
    Sutton, R., Barto, A.: Reinforcement learning: an introduction. Adaptive computation and machine learning. MIT Press (1998)Google Scholar
  50. 50.
    Taieb, S.B., Bontempi, G., Sorjamaa, A., Lendasse, A.: Long-term prediction of time series by combining direct and mimo strategies. In: Proc. of the 2009 IJCNN, pp. 1559–1566. IEEE Press, Piscataway (2009)Google Scholar
  51. 51.
    Wang, W., Yang, J., Muntz, R.: Temporal association rules on evolving numerical attributes. In: Proc. of the 17th ICDE, pp. 283–292. IEEE CS, USA (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rui Henriques
    • 1
  • Cláudia Antunes
    • 1
  1. 1.D2PM, IST–UTLPortugal

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