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Investigating Clinical Care Pathways Correlated with Outcomes

  • Geetika T. Lakshmanan
  • Szabolcs Rozsnyai
  • Fei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8094)

Abstract

Clinical care pathway analysis is the process of discovering how clinical activities impact patients in their care journeys, and uses the discovered knowledge for various applications including the redesign and optimization of clinical pathways. We present an approach for mining clinical care pathways correlated with patient outcomes that involves a combination of clustering, process mining and frequent pattern mining. Our approach is implemented as a set of interactive tools in the business process insight (BPI) platform, a a collaborative software as a service platform, that provides an event-driven process-aware analytics toolset. After interactively utilizing the individual clustering, process mining, and frequent pattern mining capabilities in BPI, users can overlay frequent patterns, ranked according to their correlation with a particular patient outcome, on a mined model of the patient population with that outcome. We have tested our approach for mining care pathways correlated with outcomes on electronic medical record data obtained from a US based healthcare provider on congestive heart failure (CHF) patients. Experimental results show that the tools we have developed and implemented can provide new insights to facilitate the improvement of existing clinical care pathways.

Keywords

Care pathway clustering frequent pattern mining process mining 

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, San Francisco, CA, USA (1994)Google Scholar
  2. 2.
    Aiolli, F., Burattin, A., Sperduti, A.: A business process metric based on the alpha algorithm relations. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 141–146. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Weijters, A.J.M.M., van der Aalst, W., de Medeiros, A.A.: Process mining with the heuristics miner-algorithm. BETA Working Paper (2006)Google Scholar
  4. 4.
    C.P., et al.: Searching electronic health records for temporal patterns in patient histories: A case study with microsoft amalga. In: AMIA Annual Symposium, pp. 601–605 (2008)Google Scholar
  5. 5.
    Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: KDD, pp. 429–435. ACM Press (2002)Google Scholar
  6. 6.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: Towards improving process mining results. In: SDM, pp. 401–412 (2009)Google Scholar
  7. 7.
    Jagadeesh Chandra Bose, R.P., van der Aalst, W.: Trace alignment in process mining: Opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227–242. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Caron, F., Vanthienen, J., De Weerdt, J., Baesens, B.: Advanced care-flow mining and analysis. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011 Workshops, Part I. LNBIP, vol. 99, pp. 167–168. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: ICDE, pp. 169–178 (2008)Google Scholar
  10. 10.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Computer (6), 226–231 (1996)Google Scholar
  11. 11.
    Fails, J.A., Karlson, A.K., Shahamat, L., Shneiderman, B.: A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. In: IEEE VAST, pp. 167–174 (2006)Google Scholar
  12. 12.
    Goodman, S.N.: Toward evidence-based medical statistics. 1: The p value fallacy. Annals of Internal Medicine 130, 995–1004 (1999)CrossRefGoogle Scholar
  13. 13.
    Greco, G., Guzzo, A., Pontieri, L., Saccá, D.: Mining expressive process models by clustering workflow traces. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 52–62. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Huang, Z., Lu, X., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artif. Intell. Med. 56(1), 35–50 (2012)CrossRefGoogle Scholar
  15. 15.
    Huang, Z., Lu, X., Duan, H.: Using recommendation to support adaptive clinical pathways. Journal of Medical Systems 36(3), 1849–1860 (2012)CrossRefGoogle Scholar
  16. 16.
    Ireson, C.L.: Critical pathways: Effectiveness in achieving patient outcomes. Nursing Administration 27(6), 16–23 (1997)CrossRefGoogle Scholar
  17. 17.
    Jung, J.-Y., Bae, J.: Workflow clustering method based on process similarity. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3981, pp. 379–389. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Kastner, M., Wagdy Saleh, M., Wagner, S., Affenzeller, M., Jacak, W.: Heuristic methods for searching and clustering hierarchical workflows. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 737–744. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Lakshmanan, G., Khalaf, R.: Leveraging process mining techniques to analyze semi-structured processes. IT Professional PP (99), 1–1 (2012)Google Scholar
  20. 20.
    Lang, M., Bürkle, T., Laumann, S., Prokosch, H.U.: Process mining for clinical workflows: Challenges and current limitations. In: MIE, pp. 229–234 (2008)Google Scholar
  21. 21.
    de Leoni, M., Adams, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Visual support for work assignment in process-aware information systems: Framework formalisation and implementation. Decision Support Systems 54(1), 345–361 (2012)CrossRefGoogle Scholar
  22. 22.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707–710 (1966)Google Scholar
  23. 23.
    Ren Lin, F., Chao Chou, S.: Mining time dependency patterns in clinical pathways. International Journal of Medical Informatics, 11–25 (2001)Google Scholar
  24. 24.
    Lo, D., Cheng, H.: Lucia: Mining closed discriminative dyadic sequential patterns. In: International Conference on Extending Database Technology, pp. 21–32 (2011)Google Scholar
  25. 25.
    Mans, R.S., Schonenberg, H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare - a case study in a dutch hospital. In: BIOSTEC (Selected Papers), pp. 425–438 (2008)Google Scholar
  26. 26.
    Mans, R., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: Data challenges when answering frequently posed questions. In: ProHealth/KR4HC, pp. 140–153 (2012)Google Scholar
  27. 27.
    Moskovitch, R., Shahar, Y.: Medical temporal-knowledge discovery via temporal abstraction. In: AMIA Annual Symposium, pp. 452–456 (2009)Google Scholar
  28. 28.
    Norén, G.N., Bate, A., Hopstadius, J., Star, K., Edwards, I.R.: Temporal pattern discovery for trends and transient effects: its application to patient records. In: SIGKDD, pp. 963–971. ACM (2008)Google Scholar
  29. 29.
    Perimal-Lewis, L.: Gaining insight from patient journey data using a process-oriented analysis approach. In: HIKM 2012, vol. 129, pp. 59–66 (2012)Google Scholar
  30. 30.
    Poelmans, J., Dedene, G., Verheyden, G., Van der Mussele, H., Viaene, S., Peters, E.: Combining business process and data discovery techniques for analyzing and improving integrated care pathways. In: Perner, P. (ed.) ICDM 2010. LNCS, vol. 6171, pp. 505–517. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  31. 31.
    Qiao, M., Akkiraju, R., Rembert, A.J.: Towards efficient business process clustering and retrieval: Combining language modeling and structure matching. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 199–214. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  32. 32.
    Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: A methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012)CrossRefGoogle Scholar
  33. 33.
    Rozsnyai, S., Lakshmanan, G.T., Muthusamy, V., Khalaf, R., Duftler, M.J.: Business process insight: An approach and platform for the discovery and analysis of end-to-end business processes. In: SRII Global Conference, pp. 80–89 (2012)Google Scholar
  34. 34.
    Silva, V., Fernando Chirigati, K.M.A.O., de Oliveira, D., Braganholo, V., Murta, L., Mattoso, M.: Similarity-based workflow clustering. Journal of Computational Interdisciplinary Sciences 2(1), 23–35 (2011)CrossRefGoogle Scholar
  35. 35.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)Google Scholar
  36. 36.
    Weerdt, J.D., Caron, F., Vanthienen, J., Baesens, B.: Getting a grasp on clinical pathway data: An approach based on process mining. In: PAKDD Workshops, pp. 22–35 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Geetika T. Lakshmanan
    • 1
  • Szabolcs Rozsnyai
    • 1
  • Fei Wang
    • 1
  1. 1.IBM T.J. Watson Research CenterHawthorneUSA

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