Skip to main content
SpringerLink
Log in

Coping with “Exceptional” Patients in META-GLARE

  • Conference paper
  • First Online:
Book cover Biomedical Engineering Systems and Technologies (BIOSTEC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1024))

Abstract

Many different computer-assisted management systems for Computer Interpretable Guidelines (CIGs) have been developed. While CIGs propose evidence-based treatments of “typical” patients, exceptions may arise, as well the need to cope with comorbidities. Though the treatment of both phenomena involves a deviation from the “standard” execution of CIGs, until now they have been managed as different problems, and no homogeneous approach to cope with both of them has been devised. In this paper we present the extensions to META-GLARE to overcome such a limitation. To achieve such a goal, we propose a modular architecture supporting the concurrent execution of multiple guidelines, integrated with an ontological knowledge base and with several reasoning mechanisms, including temporal reasoning and goal-based planning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This choice is, in our opinion, quite surprising, since there does not seem to be a clear cut between the two phenomena. Just as one prototypical example, in [17] heart failure is considered as an “exception” for a patient treated with a CIG for trauma. But, when a patient with a trauma manifests a heart failure, s\he becomes a comorbid patient, and attention must be paid to avoid dangerous interactions between the treatment (CIG) for the trauma and the treatment (CIG) for the hearth failure.

  2. 2.

    CIGs may consist of hundreds of actions and\or alternative paths. An extensive check of all interactions could provide a combinatorial number of cases, most of which are not interesting for the patient at hand. Physician-driven focusing is an essential step to avoid an unnecessary combinatorial explosion of the computation and of the number of the identified interactions.

  3. 3.

    As an example, a possible undesired interaction between the actions Act1 in CIGA and Act2 in CIGB can be detected and physician can choose to manage it via the substitution of Act2 with a set of actions achieving the goal of Act2, but non-interacting with Act1. However, such a substitution must be performed only in case the execution of the two CIGs enforces the execution of both Act1 and Act2 (at times such that their effects may overlap in time). Indeed, if in CIGA a path of actions not including Act1 has been selected for execution, there is no need to substitute Act2.

References

  1. Field, M.J., Lohr, K.N. (eds.): Clinical practice guidelines: directions for a new program. National Academies Press (US), Washington (DC) (1990)

    Google Scholar 

  2. Peleg, M., et al.: Comparing computer-interpretable guideline models: a case-study approach. JAMIA. 10, 52–68 (2003)

    Google Scholar 

  3. Bottrighi, A., et al.: Analysis of the GLARE and GPROVE approaches to clinical guidelines. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds.) KR4HC 2009. LNCS (LNAI), vol. 5943, pp. 76–87. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11808-1_7

    Chapter  Google Scholar 

  4. Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46, 744–763 (2013)

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Bottrighi, A., Rubrichi, S., Terenziani, P.: META-GLARE: a meta-engine for executing computer interpretable guidelines. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds.) KR4HC 2015. LNCS (LNAI), vol. 9485, pp. 37–50. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26585-8_3

    Chapter  Google Scholar 

  7. 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, 171–195 (2006)

    Article  Google Scholar 

  8. Stantic, B., Terenziani, P., Governatori, G., Bottrighi, A., Sattar, A.: An implicit approach to deal with periodically repeated medical data. Artif. Intell. Med. 55, 149–162 (2012)

    Article  Google Scholar 

  9. Anselma, L., Bottrighi, A., Montani, S., Terenziani, P.: Managing proposals and evaluations of updates to medical knowledge: theory and applications. J. Biomed. Inform. 46, 363–376 (2013)

    Article  Google Scholar 

  10. Bottrighi, A., Giordano, L., Molino, G., Montani, S., Terenziani, P., Torchio, M.: Adopting model checking techniques for clinical guidelines verification. Artif. Intell. Med. 48, 1–19 (2010)

    Article  Google Scholar 

  11. Montani, S., Terenziani, P.: Exploiting decision theory concepts within clinical guideline systems: toward a general approach. Int. J. Intell. Syst. 21, 585–599 (2006)

    Article  Google Scholar 

  12. Anselma, L., Bottrighi, A., Molino, G., Montani, S., Terenziani, P., Torchio, M.: Supporting knowledge-based decision making in the medical context: the GLARE approach. IJKBO 1, 42–60 (2011)

    Google Scholar 

  13. Terenziani, P., Montani, S., Bottrighi, A., Torchio, M., Molino, G., Correndo, G.: A context-adaptable approach to clinical guidelines. Stud. Health Technol. Inform. 107, 169–173 (2004)

    Google Scholar 

  14. 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 (LNAI), vol. 7885, pp. 17–22. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38326-7_3

    Chapter  Google Scholar 

  15. Fraccaro, P., Castelerio, M.A., Ainsworth, J., Buchan, I.: Adoption of clinical decision support in multimorbidity: a systematic review. JMIR Med. Inform. 3, e4 (2015)

    Article  Google Scholar 

  16. Riaño, D., Ortega, W.: Computer technologies to integrate medical treatments to manage multimorbidity. J. Biomed. Inform. 75, 1–13 (2017)

    Article  Google Scholar 

  17. Leonardi, G., Bottrighi, A., Galliani, G., Terenziani, P., Messina, A., Corte, F.D.: Exceptions handling within GLARE clinical guideline framework. In: AMIA (2012)

    Google Scholar 

  18. 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.) KR4HC/ProHealth -2012. LNCS (LNAI), vol. 7738, pp. 30–41. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36438-9_2

    Chapter  Google Scholar 

  19. Sánchez-Garzón, I., Fdez-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 (LNAI), vol. 7885, pp. 23–27. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38326-7_4

    Chapter  Google Scholar 

  20. Piovesan, L., Molino, G., Terenziani, P.: Supporting multi-level user-driven detection of guideline interactions. In: Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pp. 413–422. Scitepress (2015)

    Google Scholar 

  21. Piovesan, L., Molino, G., Terenziani, P.: Supporting physicians in the detection of the interactions between treatments of co-morbid patients. In: Healthcare Informatics and Analytics: Emerging Issues and Trends, pp. 165–193. IGI Global (2014)

    Google Scholar 

  22. Anselma, L., Piovesan, L., Terenziani, P.: Temporal detection and analysis of guideline interactions. Artif. Intell. Med. 76, 40–62 (2017)

    Article  Google Scholar 

  23. Bottrighi, A., Piovesan, L., Terenziani, P.: A general framework for the distributed management of exceptions and comorbidities. In: Zwiggelaar, R., Gamboa, H., Fred, A.L.N., Badia, S.B.I (eds.) Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), vol. 5, HEALTHINF, Funchal, Madeira, Portugal, 19–21 January 2018, pp. 66–76. SciTePress (2018)

    Google Scholar 

  24. Fox, J., Johns, N., Rahmanzadeh, A.: Disseminating medical knowledge: the PROforma approach. Artif. Intell. Med. 14, 157–182 (1998)

    Article  Google Scholar 

  25. Shahar, Y., Miksch, S., Johnson, P.: The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif. Intell. Med. 14, 29–51 (1998)

    Article  Google Scholar 

  26. Sutton, D.R., Fox, J.: The syntax and semantics of the PROforma guideline modeling language. J. Am. Med. Inform. Assoc. JAMIA 10, 433–443 (2003)

    Article  Google Scholar 

  27. Tu, S.W., Musen, M.A.: A flexible approach to guideline modeling. In: Proceedings of the AMIA Symposium, pp. 420–424 (1999)

    Google Scholar 

  28. Grando, A., Peleg, M., Glasspool, D.: A goal-oriented framework for specifying clinical guidelines and handling medical errors. J. Biomed. Inform. 43, 287–299 (2010)

    Article  Google Scholar 

  29. Quaglini, S., Stefanelli, M., Lanzola, G., Caporusso, V., Panzarasa, S.: Flexible guideline-based patient careflow systems. Artif. Intell. Med. 22, 65–80 (2001)

    Article  Google Scholar 

  30. Peleg, M., Somekh, J., Dori, D.: A methodology for eliciting and modeling exceptions. J. Biomed. Inform. 42, 736–747 (2009)

    Article  Google Scholar 

  31. Zamborlini, V., Hoekstra, R., da Silveira, M., Pruski, C., ten Teije, A., van Harmelen, F.: A conceptual model for detecting interactions among medical recommendations in clinical guidelines. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 591–606. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13704-9_44

    Chapter  Google Scholar 

  32. Wilk, S., Michalowski, M., Michalowski, W., Rosu, D., Carrier, M., Kezadri-Hamiaz, M.: Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines. J. Biomed. Inform. 66, 52–71 (2017)

    Article  Google Scholar 

  33. Riaño, D., Collado, A.: Model-based combination of treatments for the management of chronic comorbid patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS (LNAI), vol. 7885, pp. 11–16. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38326-7_2

    Chapter  Google Scholar 

  34. 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). https://doi.org/10.1007/978-3-642-38326-7_5

    Chapter  Google Scholar 

  35. Merhej, E., Schockaert, S., McKelvey, T.G., De Cock, M.: Generating conflict-free treatments for patients with comorbidity using ASP. In: KR4HC 2016. pp. 93–100 (2016)

    Google Scholar 

  36. Zhang, Y., Zhang, Z.: Preliminary result on finding treatments for patients with comorbidity. In: Miksch, S., Riaño, D., ten Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 14–28. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13281-5_2

    Chapter  Google Scholar 

  37. International Health Terminology Standards Development Organisation: SNOMED Clinical Terms (2015). http://www.ihtsdo.org/snomed-ct

  38. WHO Collaborating Centre for Drug Statistics Methodology: Anatomical Therapeutic Chemical classification system. http://www.whocc.no/atc/

  39. Horvitz, E.: Uncertainty, action, and interaction: In: Pursuit of Mixed-Initiative Computing (1999)

    Google Scholar 

  40. Dechter, R., Meiri, I., Pearl, J.: Temporal constraint networks. Artif. Intell. 49, 61–95 (1991)

    Article  MathSciNet  Google Scholar 

  41. Edwards, I.R., Aronson, J.K.: Adverse drug reactions: definitions, diagnosis, and management. Lancet 356, 1255–1259 (2000)

    Article  Google Scholar 

  42. Burger, D., et al.: Clinical management of drug-drug interactions in HCV therapy: challenges and solutions. J. Hepatol. 58, 792–800 (2013)

    Article  Google Scholar 

  43. Piovesan, L., Anselma, L., Terenziani, P.: Temporal detection of guideline interactions. In: HEALTHINF 2015, pp. 40–50. Scitepress (2015)

    Google Scholar 

  44. Bottrighi, A., Molino, G., Montani, S., Terenziani, P., Torchio, M.: Supporting a distributed execution of clinical guidelines. Comput. Methods Programs Biomed. 112, 200–210 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This research is original and has a financial support of the Università del Piemonte Orientale.

Author information

Authors and Affiliations

  1. Institute Computer Science, DISIT, University of Eastern Piedmont, Alessandria, Italy

    Alessio Bottrighi, Luca Piovesan & Paolo Terenziani

Authors
  1. Alessio Bottrighi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luca Piovesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Terenziani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luca Piovesan .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, São Paulo, Brazil

    Alberto Cliquet Jr.

  2. University of Saskatchewan, Saskatoon, Canada

    Sheldon Wiebe

  3. College of Charleston, Charleston, SC, USA

    Paul Anderson

  4. University of Rome Tor Vergata, Rome, Italy

    Giovanni Saggio

  5. Aberystwyth University, Aberystwyth, UK

    Reyer Zwiggelaar

  6. LIBPhys, New University of Lisbon, Lisbon, Portugal

    Hugo Gamboa

  7. Instituto de Telecomunicações and Instituto Superior Técnico, Lisbon, Portugal

    Ana Fred

  8. Madeira Interactive Technologies Institute, Universidade da Madeira, Funchal, Portugal

    Sergi Bermúdez i Badia

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bottrighi, A., Piovesan, L., Terenziani, P. (2019). Coping with “Exceptional” Patients in META-GLARE. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29196-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29195-2

  • Online ISBN: 978-3-030-29196-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation