A Constraint-Based Approach for the Conciliation of Clinical Guidelines

  • Luca PiovesanEmail author
  • Paolo Terenziani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10022)


The medical domain often arises new challenges to Artificial Intelligence. An emerging challenge is the support for the treatment of patients affected by multiple pathologies (comorbid patients). In the medical context, clinical practice guidelines (CPGs) are usually adopted to provide physicians with evidence-based recommendations, considering only single pathologies. To support physicians in the treatment of comorbid patients, suitable methodologies must be devised to “merge” CPGs. Techniques like replanning or scheduling, traditionally adopted in AI to “merge” plans, must be extended and adapted to fit the requirements of the medical domain. In this paper, we propose a novel methodology, that we term “conciliation”, to merge multiple CPGs, supporting the treatments of comorbid patients.


Computer interpretable clinical guidelines Comorbidities Combining medical guidelines Constraint satisfaction problems 


  1. 1.
    Anselma, L., Terenziani, P., Montani, S., Bottrighi, A.: Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines. Artif. Intell. Med. 38(2), 171–195 (2006)CrossRefGoogle Scholar
  2. 2.
    Beck, J.C., Carchrae, T., Freuder, E.C., Ringwelski, G.: A space-efficient backtrack-free representation for constraint satisfaction problems. Intl. J. Artif. Intell. Tools 17(4), 703–730 (2008)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bottrighi, A., Giordano, L., Molino, G., Montani, S., Terenziani, P., Torchio, M.: Adopting model checking techniques for clinical guidelines verification. Artif. Intell. Med. 48(1), 1–19 (2010)CrossRefGoogle Scholar
  4. 4.
    López-Vallverdú, J.A., Riaño, D., Collado, A.: Rule-based combination of comorbid treatments for chronic diseases applied to hypertension, diabetes mellitus and heart failure. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds.) ProHealth 2012 and KR4HC 2012. LNCS, vol. 7738, pp. 30–41. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Committee to Advise the Public Health Service on Clinical Practice Guidelines, Institute of Medicine: Clinical Practice Guidelines Directions for a New Program. National Academy Press, Washington, D.C. (1990)Google Scholar
  6. 6.
    Fraccaro, P., Arguello Castelerio, M., Ainsworth, J., Buchan, I.: Adoption of clinical decision support in multimorbidity: a systematic review. JMIR Med. Inform. 3(1), e4 (2015)CrossRefGoogle Scholar
  7. 7.
    Freuder, E.C.: Synthesizing constraint expressions. Commun. ACM 21(11), 958–966 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Freuder, E.C.: A sufficient condition for backtrack-free search. J. ACM 29(1), 24–32 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Jafarpour, B., Abidi, S.S.R.: Merging disease-specific clinical guidelines to handle comorbidities in a clinical decision support setting. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 28–32. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Michalowski, M., Wilk, S., Michalowski, W., Lin, D., Farion, K., Mohapatra, S.: Using constraint logic programming to implement iterative actions and numerical measures during mitigation of concurrently applied clinical practice guidelines. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 17–22. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Montanari, U.: Networks of constraints: fundamental properties and applications to picture processing. Inf. Sci. 7, 95–132 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Montani, S., Terenziani, P.: Exploiting decision theory concepts within clinical guideline systems: toward a general approach. Int. J. Intell. Syst. 21(6), 585–599 (2006)CrossRefzbMATHGoogle Scholar
  13. 13.
    Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46(4), 744–763 (2013)CrossRefGoogle Scholar
  14. 14.
    Piovesan, L., Anselma, L., Terenziani, P.: Temporal detection of guideline interactions. In: Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pp. 40–50. Scitepress (2015)Google Scholar
  15. 15.
    Piovesan, L., Molino, G., Terenziani, P.: An ontological knowledge and multiple abstraction level decision support system in healthcare. Decis. Analytics 1(8), 1–24 (2014)Google Scholar
  16. 16.
    Piovesan, L., Terenziani, P.: A mixed-initiative approach to the conciliation of clinical guidelines for comorbid patients. In: Riaño, D., et al. (eds.) KR4HC/ProHealth 2015. LNCS, vol. 9485, pp. 95–108. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26585-8_7 CrossRefGoogle Scholar
  17. 17.
    Sánchez-Garzón, I., Fernández-Olivares, J., Onaindía, E., Milla, G., Jordán, J., Castejón, P.: A multi-agent planning approach for the generation of personalized treatment plans of comorbid patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 23–27. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Ten Teije, A., Miksch, S., Lucas, P. (eds.): Computer-Based Medical Guidelines and Protocols: A Primer and Current Trends, Studies in Health Technology and Informatics, vol. 139. IOS Press, Amsterdam (2008)Google Scholar
  19. 19.
    Terenziani, P., Molino, G., Torchio, M.: A modular approach for representing and executing clinical guidelines. Artif. Intell. Med. 23(3), 249–276 (2001)CrossRefGoogle Scholar
  20. 20.
    Terenziani, P., Montani, S., Bottrighi, A., Torchio, M., Molino, G., Correndo, G.: A context-adaptable approach to clinical guidelines. Stud. Health Technol. Inf. 107(Pt 1), 169–173 (2004)Google Scholar
  21. 21.
    Wilk, S., Michalowski, W., Michalowski, M., Farion, K., Hing, M.M., Mohapatra, S.: Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming. J. Biomed. Inform. 46(2), 341–353 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.DISIT, Institute of Computer ScienceUniversità del Piemonte OrientaleAlessandriaItaly

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