Temporal Detection of Guideline Interactions

Luca Piovesan, Luca Anselma, Paolo Terenziani


Clinical practice guidelines are widely used to support physicians, but only on individual pathologies. On the other hand, the treatment of patients affected by multiple diseases is one of the main challenges for the modern healthcare. This requires the development of new methodologies, supporting physicians in the detection of interactions between guidelines. In a previous work, we proposed a flexible and user-driven approach, helping physicians in the detection of possible interactions between guidelines, supporting focusing and analysis at multiple levels of abstractions. However, it did not cope with the fact that interactions occur in time. For instance, the effects of two actions may potentially conflict, but practical conflicts happen only if such effects overlap in time. In this paper, we extend the ontological model to deal with the temporal aspects, and the detection algorithms to cope with them. Different types of facilities are provided to physicians, supporting the analysis of interactions between both guidelines “per se”, and the concrete application of guidelines to specific patients. In both cases, different temporal facilities are provided to user physicians, based on Artificial Intelligence temporal reasoning techniques.


  1. Allen, J.F., 1983. Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), pp.832-843.
  2. Anselma, L. et al., 2006. Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines. Artificial Intelligence in Medicine, 38(2), pp.171-195.
  3. Brusoni, V. et al., 1997. Later: managing temporal information efficiently. IEEE Expert, 12(4), pp.56-64.
  4. Dechter, R., Meiri, I. and Pearl, J., 1991. Temporal Constraint Networks. Artif. Intell., 49(1-3), pp.61-95.
  5. Fridsma, D.B., 2001. Special Issue on Workflow Management and Clinical Guidelines. Journal of the American Medical Informatics Association, 22(1).
  6. Gordon, C. and Christensen, J.P. eds., 1995. Health telematics for clinical guidelines and protocols, Amsterdam, Netherlands: IOS Press.
  7. Horvitz, E., 1999. Uncertainty, Action, and Interaction: In Pursuit of Mixed-Initiative Computing. IEEE Intelligent Systems, 14(5), pp.17-20.
  8. Jafarpour, B. and Abidi, S.S.R., 2013. Merging DiseaseSpecific Clinical Guidelines to Handle Comorbidities in a Clinical Decision Support Setting. In AIME. pp. 28-32.
  9. López-Vallverdú, J.A., Riaño, D. and Collado, A., 2013. Rule-Based Combination of Comorbid Treatments for Chronic Diseases Applied to Hypertension, Diabetes Mellitus and Heart Failure. In R. Lenz et al., eds. Process Support and Knowledge Representation in Health Care. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 30-41.
  10. Michalowski, M. et al., 2013. Using Constraint Logic Programming to Implement Iterative Actions and Numerical Measures during Mitigation of Concurrently Applied Clinical Practice Guidelines. In N. Peek, R. M. Morales, and M. Peleg, eds. Artificial Intelligence in Medicine. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 17-22.
  11. Peleg, M., 2013. Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, 46(4), pp.744-763.
  12. Piovesan, L., Molino, G. and Terenziani, P., 2014. An ontological knowledge and multiple abstraction level decision support system in healthcare. Decision Analytics, 1(1).
  13. Riaño, D. and Collado, A., 2013. Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients. In Artificial Intelligence in Medicine. 14th Conference on Artificial Intelligence in Medicine. Springer, pp. 11-16.
  14. Sánchez-Garzón, I. et al., 2013. A Multi-agent Planning Approach for the Generation of Personalized Treatment Plans of Comorbid Patients. In AIME. pp. 23-27.
  15. Shahar, Y., Miksch, S. and Johnson, P., 1998. The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine, 14(1-2), pp.29-51.
  16. Ten Teije, A., Miksch, S. and Lucas, P. eds., 2008. Computer-based medical guidelines and protocols: a primer and current trends, Amsterdam, Netherlands: IOS Press.
  17. Terenziani, P., German, E. and Shahar, Y., 2008. The temporal aspects of clinical guidelines. Studies in Health Technology and Informatics, 139, pp.81-100.
  18. Vilain, M., Kautz, H. and van Beek, P., 1990. Constraint propagation algorithms for temporal reasoning: a revised report. In D. S. Weld and J. de Kleer, eds. Readings in Qualitative Reasoning About Physical Systems. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp. 373-381.
  19. Wilk, S. et al., 2013. Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming. Journal of biomedical informatics, 46(2), pp.341-353.

Paper Citation

in Harvard Style

Piovesan L., Anselma L. and Terenziani P. (2015). Temporal Detection of Guideline Interactions . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 40-50. DOI: 10.5220/0005186300400050

in Bibtex Style

author={Luca Piovesan and Luca Anselma and Paolo Terenziani},
title={Temporal Detection of Guideline Interactions},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Temporal Detection of Guideline Interactions
SN - 978-989-758-068-0
AU - Piovesan L.
AU - Anselma L.
AU - Terenziani P.
PY - 2015
SP - 40
EP - 50
DO - 10.5220/0005186300400050