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.
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References
Antunes, C.: Pattern Mining over Nominal Event Sequences using Constraint Relaxations. Ph.D. thesis, Instituto Superior Tecnico (2005)
Antunes, C.: Temporal pattern mining using a time ontology. In: EPIA, pp. 23–34. Associação Portuguesa para a Inteligência Artificial (2007)
Antunes, C.: An ontology-based framework for mining patterns in the presence of background knowledge. In: ICAI, pp. 163–168. PTP, Beijing (2008)
Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order markov models. J. Artif. Int. Res. 22, 385–421 (2004)
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)
Ben Taieb, S., Sorjamaa, A., Bontempi, G.: Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomput. 73, 1950–1957 (2010)
Bengio, S., Fessant, F., Collobert, D.: Use of modular architectures for time series prediction. Neural Process. Lett. 3, 101–106 (1996)
Berthold, M., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction. Springer-Verlag New York, Inc., Secaucus (1999)
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)
Brahim-Belhouari, S., Bermak, A.: Gaussian process for nonstationary time series prediction. Computational Statistics and Data Analysis 47(4), 705–712 (2004)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)
Brown, P.J., Vannucci, M., Fearn, T.: Multivariate bayesian variable selection and prediction. Journal of the Royal Statistical Society 60(3), 627–641 (1998)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)
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)
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)
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)
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)
Dietterich, T.G., Michalski, R.S.: Discovering patterns in sequences of events. Artif. Intell. 25, 187–232 (1985)
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)
Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: 5th ACM SIGKDD, KDD, pp. 43–52. ACM, NY (1999)
Eddy, S.R.: Profile hidden markov models. Bioinformatics/Computer Applications in the Biosciences 14, 755–763 (1998)
Fang, Y., Koreisha, S.G.: Updating arma predictions for temporal aggregates. Journal of Forecasting 23(4), 275–296 (2004)
Freksa, C.: Temporal reasoning based on semi-intervals. A. Int. 54, 199–227 (1992)
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)
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)
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)
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)
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)
Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. John Wiley & Sons, Inc., NY (2002)
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)
Koch, I., Naito, K.: Prediction of multivariate responses with a selected number of principal components. Comput. Stat. Data Anal. 54, 1791–1807 (2010)
Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)
Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana-academy Proceedings in Engineering Sciences 31, 173–198 (2006)
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)
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)
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)
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)
Mannila, H., Toivonen, H., Inkeri Verkamo, A.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1, 259–289 (1997)
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)
Mörchen, F.: Time series knowledge mining. W. in Dissertationen, G&W (2006)
Mörchen, F.: Tutorial cidm-t temporal pattern mining in symbolic time point and time interval data. In: CIDM. IEEE (2009)
Quinlan, J.R.: Learning with continuous Classes. In: 5th Australian Joint Conf. on Artificial Intelligence, pp. 343–348 (1992)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowl. and Data Eng. 8, 970–974 (1996)
Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomput. 70, 2861–2869 (2007)
Sorjamaa, A., Lendasse, A.: Time series prediction using dirrec strategy. In: ESANN, pp. 143–148 (2006)
Sun, R., Giles, C.L.: Sequence learning: From recognition and prediction to sequential decision making. IEEE Intelligent Systems 16, 67–70 (2001)
Sun, R., Peterson, T.: Autonomous learning of sequential tasks: experiments and analyses. IEEE Transactions on Neural Networks 9(6), 1217–1234 (1998)
Sutton, R.S.: Learning to predict by the methods of temporal differences. Machine Learning 3, 9–44 (1988)
Sutton, R., Barto, A.: Reinforcement learning: an introduction. Adaptive computation and machine learning. MIT Press (1998)
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)
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)
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Henriques, R., Antunes, C. (2012). An Integrated Approach for Healthcare Planning over Multi-dimensional Data Using Long-Term Prediction. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_6
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DOI: https://doi.org/10.1007/978-3-642-29361-0_6
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