Abstract
In this paper, we tackle the issue of assessing the effectiveness of sequences of treatments by introducing the concept of state-changing sequential patterns. Our proposal aims at identifying sequential patterns in an environment where certain actions are taken for patients (medical procedures, administration of pharmaceuticals, etc.) while simultaneously measuring some indicator of their health (e.g., blood pressure). We propose to combine the information about the events with the information about the states of the patients targeted by these events when mining for sequential patterns. To be able to properly interpret the changes in states as outcomes of sequences of events, we rely on the concept of a control group known from clinical trials. We illustrate the usefulness of our proposal with a proof-of-concept experiment.
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References
Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th ICDE, pp. 3–14 (1995)
Giannotti, F., Nanni, M., Pedreschi, D.: Efficient mining of temporally annotated sequences. In: SIAM International Conference on Data Mining, pp. 348–359 (2006)
Gebser, M., Guyet, T., Quiniou, R., Romero, J., Schaub, T.: Knowledge-based sequence mining with ASP. In: Proceedings of the 25th IJCAI, pp. 1497–1504 (2016)
Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U.: Multi-dimensional sequential pattern mining. In: Proceedings of the 10th CIKM, pp. 81–88 (2001)
Plantevit, M., Laurent, A., Laurent, D., Teisseire, M., Choong, Y.W.: Mining multidimensional and multilevel sequential patterns. ACM Trans. Knowl. Discov. Data (TKDD) 4, 1–37 (2010)
Fowkes, J., Sutton, C.: A subsequence interleaving model for sequential pattern mining. In: Proceedings of the 22nd ACM SIGKDD, pp. 835–844 (2016)
Li, T., Webb, G.I., Petitjean, F.: Exact discovery of the most interesting sequential patterns. CoRR abs/1506.08009 (2015)
Guidotti, R., Rossetti, G., Pappalardo, L., Giannotti, F., Pedreschi, D.: Market basket prediction using user-centric temporal annotated recurring sequences. In: Proceedings of the 33rd ICDM, vol. 00, pp. 895–900 (2018)
Kahn, M.: UCI Machine Learning Repository (1994)
Gay, P., López, B., Meléndez, J.: Learning complex events from sequences with informed gaps. In: ICMLA, pp. 1089–1094. IEEE (2015)
Acknowledgments
This research is partly funded by the Polish National Science Center under Grant No. DEC-2015/19/B/ST6/02637.
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Piernik, M., Solomiewicz, J., Jachnik, A. (2019). Assessing the Effectiveness of Sequences of Treatments Using Sequential Patterns. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_18
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DOI: https://doi.org/10.1007/978-3-030-21642-9_18
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