Discovering Probabilistic Structures of Healthcare Processes

  • Arjen Hommersom
  • Sicco Verwer
  • Peter J. F. Lucas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8268)


Medical protocols and guidelines can be looked upon as concurrent programs, where the patient’s state dynamically changes over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into the correctness of guidelines and protocols by adopting a logical point of view. However, there is uncertainty involved both in the management of the disease and the way the disease will develop, and, therefore, a probabilistic view on medical protocols seems more appropriate. Representations using Bayesian networks capture that uncertainty, but usually concern a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learning to represent and identify patient groups for obtaining insight into the care that patients have received. We evaluate this approach using data obtained from general practitioners and identify significant differences in patients who were diagnosed with a transient ischemic attack. Finally, we discuss the implications of such a computational method for the analysis of medical protocols and guidelines.


Clinical guidelines temporal knowledge representations knowledge extraction from healthcare databases 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    ten Teije, A., Marcos, M., Balser, M., van Croonenborgd, J., Duellic, C., van Harmelena, F., Lucas, P., Miksch, S., Reif, W., Rosenbrand, K., Seyfang, A.: Improving medical protocols by formal methods. Artificial Intelligence in Medicine 63(3), 193–209 (2006)CrossRefGoogle Scholar
  2. 2.
    Hommersom, A., Groot, P., Lucas, P., Balser, M., Schmitt, J.: Verification of medical guidelines using background knowledge in task networks. IEEE Transactions on Knowledge and Data Engineering 19(6), 832–846 (2007)CrossRefGoogle Scholar
  3. 3.
    Bottrighi, A., Giordano, L., Molino, G., Montani, S., Terenziani, P., Torchio, M.: Adopting model checking techniques for clinical guidelines verification. Artificial Intelligence in Medicine 48(1), 1–19 (2010)CrossRefGoogle Scholar
  4. 4.
    Quaglini, S.: Compliance with clinical practice guidelines. In: Teije, A.T., Miksch, S., Lucas, P. (eds.) Computer-Based Medical Guidelines and Protocols: A Primer and Current Trends. Studies in Health Technology and Informatics, vol. 139, pp. 160–179. IOS Press (2008)Google Scholar
  5. 5.
    Field, M., Lohr, K. (eds.): Clinical Practice Guidelines: Directions for a New Program. National Academy Press, Institute of Medicine, Washington, D.C (1990)Google Scholar
  6. 6.
    ten Teije, A., Miksch, S., Lucas, P. (eds.): Computer-based Clinical Guidelines and Protocols: a Primer and Current Trends. IOS Press, Amsterdam (2008)Google Scholar
  7. 7.
    Fox, J., Das, S.: Safe and Sound: Artificial Intelligence in Hazardous Applications. AAAI Press (2000)Google Scholar
  8. 8.
    Peleg, M., Boxwala, A., Ogunyemi, O., Zeng, P., Tu, S., Lacson, R., Begnstam, E., Ash, N.: GLIF3: The evolution of a guideline representation format. In: Proc. AMIA Annual Symposium, pp. 645–649 (2000)Google Scholar
  9. 9.
    Fox, J., Johns, N., Rahmanzadeh, A., Thomson, R.: PROforma: a general technology for clinical decision support systems. Computer Methods and Programs in Biomedicine 54, 59–67 (1997)CrossRefGoogle Scholar
  10. 10.
    Shahar, Y., Miksch, S., Johnson, P.: The Asgaard project: A task-specific framework for the application and critiquing of time-orientied clinical guidelines. Artificial Intelligence in Medicine 14, 29–51 (1998)CrossRefGoogle Scholar
  11. 11.
    Tu, S., Musen, M.: From guideline modeling to guideline execution: Defining guideline based decision-support services. In: Proceedings of American Medical Informatics Association Symposium, Los Angeles, CA, pp. 863–867 (1999)Google Scholar
  12. 12.
    Hommersom, A., Groot, P., Lucas, P., Balser, M., Schmitt, J.: Verification of medical guidelines using background knowledge in task networks. IEEE Transactions on Knowledge and Data Engineering 19(6), 832–846 (2007)CrossRefGoogle Scholar
  13. 13.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  14. 14.
    Andreassen, S.: Planning of therapy and tests in causal probabilistic networks. Artificial Intelligence in Medicine 4(3), 227–241 (1992)CrossRefGoogle Scholar
  15. 15.
    Lucas, P., van der Gaag, L., Abu-Hanna, A.: Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine 30, 201–214 (2004)CrossRefGoogle Scholar
  16. 16.
    Dagum, P., Galper, A., Horvitz, E.: Dynamic network models for forecasting. In: Proceedings of UAI 1992, pp. 41–48 (1992)Google Scholar
  17. 17.
    Neapolitan, R.: Learning Bayesian Networks. Pearson (2004)Google Scholar
  18. 18.
    Robinson, R.: Counting unlabeled acyclic graphs. In: LNM, vol. 622, pp. 220–227. Springer, NY (1977)Google Scholar
  19. 19.
    Gillespie, S.B., Perlman, M.D.: Enumerating Markov Equivalence Classes of Acyclic Digraph Models. In: UAI 2001 (2001)Google Scholar
  20. 20.
    Ghahramani, Z.: Learning dynamic bayesian networks. In: Giles, C.L., Gori, M. (eds.) IIASS-EMFCSC-School 1997. LNCS (LNAI), vol. 1387, pp. 168–197. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  21. 21.
    Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Trans. Softw. Eng. Methodol. 7, 215–249 (1998)CrossRefGoogle Scholar
  22. 22.
    Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines - a survey. Proceedings of the IEEE 84, 1090–1123 (1996)CrossRefGoogle Scholar
  23. 23.
    Bertolino, A., Inverardi, P., Pelliccione, P., Tivoli, M.: Automatic synthesis of behavior protocols for composable web-services. In: Proceedings of the Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, pp. 141–150. ACM (2009)Google Scholar
  24. 24.
    Aarts, F., Schmaltz, J., Vaandrager, F.: Inference and abstraction of the biometric passport. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 673–686. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Walkinshaw, N., Bogdanov, K., Holcombe, M., Salahuddin, S.: Reverse engineering state machines by interactive grammar inference. In: Proceedings of the 14th Working Conference on Reverse Engineering, pp. 209–218. IEEE (2007)Google Scholar
  26. 26.
    de la Higuera, C.: Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, New York (2010)Google Scholar
  27. 27.
    Sudkamp, T.A.: Languages and Machines: an introduction to the theory of computer science, 3rd edn. Addison-Wesley (2006)Google Scholar
  28. 28.
    Dupont, P., Denis, F., Esposito, Y.: Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms. Pattern Recognition 38, 1349–1371 (2005)CrossRefzbMATHGoogle Scholar
  29. 29.
    Boutilier, C., Dearden, R., Goldszmidt, M.: Exploiting structure in policy construction. In: IJCAI. AAAI (1995)Google Scholar
  30. 30.
    Geiger, D., Heckerman, D.: Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence 82, 45–74 (1996)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Visscher, S., Lucas, P.J.F., Flesch, I., Schurink, K.: Using temporal context-specific independence information in the exploratory analysis of disease processes. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 87–96. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  32. 32.
    Gutierrez, J., Ramirez, G., Rundek, T., Sacco, R.L.: Statin therapy in the prevention of recurrent cardiovascular events: a sex-based meta-analysis. Arch. Intern. Med. 172(12), 909–919 (2012)CrossRefGoogle Scholar
  33. 33.
    Duivesteijn, W., Knobbe, A., Feelders, A., van Leeuwen, M.: Subgroup discovery meets bayesian networks – an exceptional model mining approach. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pp. 158–167. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  34. 34.
    Bohada, J.A., Riaño, D., López-Vallverdú, J.A.: Automatic generation of clinical algorithms within the state-decision-action model. Expert Systems with Applications 39(12), 10709–10721 (2012)Google Scholar
  35. 35.
    López-Vallverdú, J.A., Riaño, D., Bohada, J.A.: Improving medical decision trees by combining relevant health-care criteria. Expert Systems with Applications 39(14), 11782–11791 (2012)Google Scholar
  36. 36.
    Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  37. 37.
    Mans, R.S., 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: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds.) ProHealth 2012 and KR4HC 2012. LNCS, vol. 7738, pp. 140–153. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  38. 38.
    Kaymak, U., Mans, R., van de Steeg, T., Dierks, M.: On process mining in health care. In: SMC, pp. 1859–1864 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arjen Hommersom
    • 1
  • Sicco Verwer
    • 1
  • Peter J. F. Lucas
    • 1
  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenThe Netherlands

Personalised recommendations