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Mining Clinical Process in Order Histories Using Sequential Pattern Mining Approach

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7867))

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Abstract

In hospital information system, order-entry system is used to transfer the orders from doctors or nurses to other medical stuffs. Thus, since order histories will store the clinincal process of each doctors in a sequential way, reuse of such data will capture the process of each clinician. This paper applied two types of sequential pattern mining approaches to analysis of a sequential process for each care process. The empirical results show the methods enable us to capture the temporal characteristics of behavior of clinicians, which will give a subset of decision making process in clinical environments.

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References

  1. Currie, L.M., Desjardins, K.S., Stone, P.W., Lai, T.Y., Schwartz, E., Schnall, R., Bakken, S.: Near-miss and hazard reporting: promoting mindfulness in patient safety education. Studies in Health Technology and Informatics 129(pt. 1), 285–290 (2007)

    Google Scholar 

  2. Magrabi, F., McDonnell, G., Westbrook, J., Coiera, E.: Using an accident model to design safe electronic medication management systems. Stud. Health Technol. Inform. 129, 948–952 (2007)

    Google Scholar 

  3. Bates, D.W.: Computerized physician order entry and medication errors: finding a balance. J. of Biomedical Informatics 38, 259–261 (2005)

    Article  Google Scholar 

  4. Koppel, R., Metlay, J.P., Cohen, A., Abaluck, B., Localio, A.R., Kimmel, S.E., Strom, B.L.: Role of computerized physician order entry systems in facilitating medication errors. J. Am. Med. Assoc. 293(10), 1197–1203 (2005)

    Article  Google Scholar 

  5. Abe, H., Tsumoto, S.: Mining classification rules for detecting medication order changes by using characteristic cpoe subsequences. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 80–89. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43, 3:1–3:41 (2010)

    Google Scholar 

  7. Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)

    Google Scholar 

  8. Hirschberg, D.S.: Algorithms for the longest common subsequence problem. J. ACM 24, 664–675 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  9. Nakagawa, H.: Automatic term recognition based on statistics of compound nouns. Terminology 6(2), 195–210 (2000)

    Google Scholar 

  10. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of the 17th International Conference on Data Engineering, pp. 215–224. IEEE Computer Society, Los Alamitos (2001)

    Google Scholar 

  11. Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)

    Google Scholar 

  12. Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)

    Article  MATH  Google Scholar 

  13. Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I.H.: Using model trees for classification. Machine Learning 32(1), 63–76 (1998)

    Article  MATH  Google Scholar 

  14. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (2000)

    Google Scholar 

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Tsumoto, S., Abe, H. (2013). Mining Clinical Process in Order Histories Using Sequential Pattern Mining Approach. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_21

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  • DOI: https://doi.org/10.1007/978-3-642-40319-4_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40318-7

  • Online ISBN: 978-3-642-40319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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