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Considering Temporal Preferences and Probabilities in Guideline Interaction Analysis

  • Paolo TerenzianiEmail author
  • Antonella Andolina
Conference paper
  • 773 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)

Abstract

The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. Practically speaking, such interactions occur in time. The GLARE project explicitly provides temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, (i) the actions in the guidelines can be executed by physicians at different times with different preferences, and that (ii) the effects of such actions have a probabilistic distribution in time. In our approach, physicians may investigate what are the preferences of their choices on the execution-time of guideline actions, and the probabilities that their effects temporally intersect (interactions may occur only in case effects intersect in time).

Keywords

Comorbidities CIG interactions Temporal reasoning Probabilities Preferences 

References

  1. 1.
    Ten Teije, A., Miksch, S., Lucas, P. (eds.): Computer-Based Medical Guidelines and Protocols: A Primer and Current Trends. IOS Press, Amsterdam (2008)Google Scholar
  2. 2.
    Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46, 744–763 (2013)CrossRefGoogle Scholar
  3. 3.
    Bottrighi, A., Terenziani, P.: META-GLARE: a meta-system for defining your own computer interpretable guideline system - architecture and acquisition. Artif. Intell. Med. 72, 22–41 (2016)CrossRefGoogle Scholar
  4. 4.
    Riaño, D., Ortega, W.: Computer technologies to integrate medical treatments to manage multimorbidity. J. Biomed. Inform. 75, 1–13 (2017)CrossRefGoogle Scholar
  5. 5.
    Piovesan, L., Molino, G., Terenziani, P.: Supporting multi-level user-driven detection of guideline interactions. In: Proceedings of HEALTHINF, pp. 413–422 (2015)Google Scholar
  6. 6.
    Zamborlini, V., da Silveira, M., Pruski, C., ten Teije, A., van Harmelen, F.: Towards a conceptual model for enhancing reasoning about clinical guidelines. In: Miksch, S., Riaño, D., ten Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 29–44. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-13281-5_3CrossRefGoogle Scholar
  7. 7.
    Piovesan, L., Terenziani, P., Molino, G.: GLARE-SSCPM: an intelligent system to support the treatment of comorbid patients. IEEE Intell. Syst. 33(6), 37–46 (2018)CrossRefGoogle Scholar
  8. 8.
    Terenziani, P.: Irregular indeterminate repeated facts in temporal relational databases. IEEE Trans. Knowl. Data Eng. 28(4), 1075–1079 (2016)CrossRefGoogle Scholar
  9. 9.
    Anselma, L., Piovesan, L., Sattar, A., Stantic, B., Terenziani, P.: A comprehensive approach to ‘Now’ in temporal relational databases: semantics and representation. IEEE Trans. Knowl. Data Eng. 28(10), 2538–2551 (2016)CrossRefGoogle Scholar
  10. 10.
    Terenziani, P.: Nearly periodic facts in temporal relational databases. IEEE Trans. Knowl. Data Eng. 28(10), 2822–2826 (2016)CrossRefGoogle Scholar
  11. 11.
    Anselma, L., Piovesan, L., Terenziani, P.: Temporal detection and analysis of guideline interactions. Artif. Intell. Med. 76, 40–62 (2017)CrossRefGoogle Scholar
  12. 12.
    Terenziani, P., Andolina, A., Piovesan, L.: Managing temporal constraints with preferences: representation, reasoning, and querying. IEEE Trans. Knowl. Data Eng. 29(9), 2067–2071 (2017)CrossRefGoogle Scholar
  13. 13.
    Andolina, A., Anselma, L., Piovesan, L., Terenziani, P.: Querying probabilistic temporal constraints for guideline interaction analysis: GLARE’s approach. In: Simari, G.R., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J.A. (eds.) IBERAMIA 2018. LNCS (LNAI), vol. 11238, pp. 3–15. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03928-8_1CrossRefGoogle Scholar
  14. 14.
    Dechter, R., Meiri, I., Pearl, J.: Temporal constraint networks. Artif. Intell. 49, 61–95 (1991)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DISIT, Institute of Computer ScienceUniversità del Piemonte OrientaleAlessandriaItaly
  2. 2.ITCS SommeillerTurinItaly

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