Generating Conflict-Free Treatments for Patients with Comorbidity Using Answer Set Programming

  • Elie Merhej
  • Steven SchockaertEmail author
  • T. Greg McKelveyEmail author
  • Martine De CockEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10096)


Conflicts in recommended medical interventions regularly arise when multiple treatments are simultaneously needed for patients with comorbid diseases. An approach that can automatically repair such inconsistencies and generate conflict-free combined treatments is thus a valuable aid for clinicians. In this paper we propose an answer set programming based method that detects and repairs conflicts between treatments. The answer sets of the program directly correspond to proposed treatments, accounting for multiple possible solutions if they exist. We also include the possibility to take preferences based on drug-drug interactions into account while solving inconsistencies. We show in a case study that our method results in more preferred treatments than standard approaches.


Duodenal Ulcer Solution Treatment Transient Ischemic Attack Candidate Treatment Task Network 
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  1. 1.
    Barr, J., Fraser, G.L., Puntillo, K., Ely, E.W., Gélinas, C., Dasta, J.F., Davidson, J.E., Devlin, J.W., Kress, J.P., Joffe, A.M., et al.: Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit. Crit. Care Med. 41(1), 263–306 (2013)CrossRefGoogle Scholar
  2. 2.
    De Clercq, P.A., Blom, J.A., Korsten, H.H., Hasman, A.: Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif. Intell. Med. 31(1), 1–27 (2004)CrossRefGoogle Scholar
  3. 3.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: a conflict-driven answer set solver. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 260–265. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72200-7_23 CrossRefGoogle Scholar
  4. 4.
    Hommersom, A., Groot, P., Lucas, P.J., Balser, M., Schmitt, J.: Verification of medical guidelines using background knowledge in task networks. IEEE Trans. Knowl. Data Eng. 19(6), 832–846 (2007)CrossRefGoogle Scholar
  5. 5.
    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 (LNAI), vol. 7885, pp. 28–32. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38326-7_5 CrossRefGoogle Scholar
  6. 6.
    Jakovljevic, M., Ostojic, L.: Comorbidity and multimorbidity in medicine today: challenges and opportunities for bringing separated branches of medicine closer to each other. Psychiatr. Danub. 25(Suppl 1), 18–28 (2013)Google Scholar
  7. 7.
    Latoszek-Berendsen, A., Tange, H., Van Den Herik, H., Hasman, A., et al.: From clinical practice guidelines to computer-interpretable guidelines. Methods Inf. Med. 49(6), 550–570 (2010)CrossRefGoogle Scholar
  8. 8.
    Lifschitz, V.: What is answer set programming? In: AAAI, vol. 8, pp. 1594–1597 (2008)Google Scholar
  9. 9.
    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., Teije, A. (eds.) KR4HC/ProHealth-2012. LNCS (LNAI), vol. 7738, pp. 30–41. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36438-9_2 CrossRefGoogle Scholar
  10. 10.
    Panel, D.: Clinical practice guidelines, vol. I. Agency for Health Care Policy and Research, Washington, DC (1993)Google Scholar
  11. 11.
    Spiotta, M., Bottrighi, A., Giordano, L., Theseider Dupré, D.: Conformance analysis of the execution of clinical guidelines with basic medical knowledge and clinical terminology. In: Miksch, S., Riaño, D., Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 62–77. Springer, Cham (2014). doi: 10.1007/978-3-319-13281-5_5 Google Scholar
  12. 12.
    Ten Teije, A., Miksch, S., Lucas, P.: Computer-Based Medical Guidelines and Protocols: A Primer and Current Trends, vol. 139. Ios Press, Amsterdam (2008)Google Scholar
  13. 13.
    Tu, S.W., Campbell, J.R., Glasgow, J., Nyman, M.A., McClure, R., McClay, J., Parker, C., Hrabak, K.M., Berg, D., Weida, T., et al.: The sage guideline model: achievements and overview. J. Am. Med. Inf. Assoc. 14(5), 589–598 (2007)CrossRefGoogle Scholar
  14. 14.
    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. Inf. 46(2), 341–353 (2013)CrossRefGoogle Scholar
  15. 15.
    Zamborlini, V., Hoekstra, R., Da Silveira, M., Pruski, C., ten Teije, A., van Harmelen, F.: Inferring recommendation interactions in clinical guidelines. Semant. Web 7(4), 421–446 (2016)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Zhang, Z.: Preliminary result on finding treatments for patients with comorbidity. In: Miksch, S., Riaño, D., Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 14–28. Springer, Cham (2014). doi: 10.1007/978-3-319-13281-5_2 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ghent UniversityGhentBelgium
  2. 2.Cardiff UniversityCardiffUK
  3. 3.University of Washington TacomaTacomaUSA
  4. 4.KenSciSeattleUSA

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