Skip to main content
SpringerLink
Log in

Monitoring Patients with Hypoglycemia Using Self-adaptive Protocol-Driven Agents: A Case Study

  • Conference paper
  • First Online:
Engineering Multi-Agent Systems (EMAS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10093))

Included in the following conference series:

Abstract

Trace expressions are a compact and expressive formalism for specifying complex patterns of actions. In this paper they have been used to model medical protocols and to generate agents able to execute them, also adapting to the context dynamics. To this aim, we extended our previous work on “self-adaptive agents driven by interaction protocols” by allowing agents to be guided by trace expressions instead of the less concise and less powerful “constrained global types”. This extension required a limited effort, which is an advantage of the previous work as it is relatively straightforward to adapt it to accommodate new requirements arising in sophisticated domains.

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.

    Clinical guidelines are special types of plans realized by collective agents.

  2. 2.

    Medical guidelines are clinical behavior recommendations used to help and support physicians in the definition of the most appropriate diagnosis and/or therapy within determinate clinical circumstances.

  3. 3.

    In our prototypical implementation, these policies are the default ones: unexpected events are discarded, no reaction is associated with perceived events, and selection selects the first action returned by the generate function.

  4. 4.

    In the Jason implementation discussed later on, we modeled these artifacts as “dumb” agents which can receive FIPA-ACL messages. This was an easy and quick way to build a working prototype including all the relevant MAS components, without needing to actually implement Java classes for the artifacts in the system.

References

  1. Aielli, F., Ancona, D., Caianiello, P., Costantini, S., Gasperis, G.D., Marco, A.D., Ferrando, A., Mascardi, V.: FRIENDLY & KIND with your health: human-friendly knowledge-intensive dynamic systems for the e-health domain. In: Bajo, J., et al. (eds.) PAAMS 2016. CCIS, vol. 616, pp. 15–26. Springer, Heidelberg (2016). doi:10.1007/978-3-319-39387-2_2

    Chapter  Google Scholar 

  2. Ancona, D., Barbieri, M., Mascardi, V.: Constrained global types for dynamic checking of protocol conformance in multi-agent systems. In: Proceedings of SAC 2013, pp. 1377–1379 (2013)

    Google Scholar 

  3. Ancona, D., Briola, D., Ferrando, A., Mascardi, V.: Global protocols as first class entities for self-adaptive agents. In: Proceedings of AAMAS 2015, pp. 1019–1029 (2015)

    Google Scholar 

  4. Ancona, D., Briola, D., Ferrando, A., Mascardi, V.: Runtime verification of fail-uncontrolled and ambient intelligence systems: a uniform approach. Intelligenza Artificiale 9(2), 131–148 (2015)

    Article  Google Scholar 

  5. Ancona, D., Dovier, A.: A theoretical perspective of coinductive logic programming. Fundamenta Informaticae 140(3–4), 221–246 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  6. Ancona, D., Drossopoulou, S., Mascardi, V.: Automatic generation of self-monitoring MASs from multiparty global session types in Jason. In: Baldoni, M., Dennis, L., Mascardi, V., Vasconcelos, W. (eds.) DALT 2012. LNCS (LNAI), vol. 7784, pp. 76–95. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37890-4_5

    Chapter  Google Scholar 

  7. Ancona, D., Ferrando, A., Mascardi, V.: Comparing trace expressions and linear temporal logic for runtime verification. In: Ábrahám, E., Bonsangue, M., Johnsen, E.B. (eds.) Essays Dedicated to Frank de Boer on the Occasion of His 60th Birthday. LNCS, vol. 9660, pp. 47–64. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30734-3_6

    Chapter  Google Scholar 

  8. Annicchiarico, R., Corts, U., Urdiales, C.: Agent Technology and e-Health. Whitestein Series in Software Agent Technologies and Autonomic Computing, 1st edn. Birkhuser, Basel (2008)

    Book  Google Scholar 

  9. Bayliss, E.A., Steiner, J.F., Fernald, D.H., Crane, L.A., Main, D.S.: Descriptions of barriers to self-care by persons with comorbid chronic diseases. Ann. Family Med. 1(1), 15–21 (2003)

    Article  Google Scholar 

  10. Bellifemine, F.L., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. Wiley, Chichester (2007)

    Book  Google Scholar 

  11. Bergenti, F., Poggi, A.: Developing smart emergency applications with multi-agent systems. Int. J. E Health Med. Comm. 1(4), 1–13 (2010)

    Article  Google Scholar 

  12. Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley, Chichester (2007)

    Book  MATH  Google Scholar 

  13. Bottrighi, A., Chesani, F., Mello, P., Montali, M., Montani, S., Storari, S., Terenziani, P.: Analysis of the GLARE and GPROVE approaches to clinical guidelines. In: Riaño, D., Teije, A., Miksch, S., Peleg, M. (eds.) KR4HC 2009. LNCS (LNAI), vol. 5943, pp. 76–87. Springer, Heidelberg (2010). doi:10.1007/978-3-642-11808-1_7

    Chapter  Google Scholar 

  14. Bricon-Souf, N., Newman, C.R.: Context awareness in health care: a review. Int. J. Med. Inf. 76(1), 2–12 (2007)

    Article  Google Scholar 

  15. Cao, Y., Yu, W., Ren, W., Chen, G.: An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans. Ind. Inf. 9(1), 427–438 (2013)

    Article  Google Scholar 

  16. Chan, V., Ray, P., Parameswaran, N.: Mobile e-health monitoring: an agent-based approach. IET Commun. 2, 223–230 (2008)

    Article  Google Scholar 

  17. Chen, S., Ho, D.W., Li, L., Liu, M.: Fault-tolerant consensus of multi-agent system with distributed adaptive protocol. IEEE Trans. Cybern. 45(10), 2142–2155 (2015)

    Article  Google Scholar 

  18. Chesani, F., Matteis, P., Mello, P., Montali, M., Storari, S.: A framework for defining and verifying clinical guidelines: a case study on cancer screening. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 338–343. Springer, Heidelberg (2006). doi:10.1007/11875604_39

    Chapter  Google Scholar 

  19. Ferrando, A.: Parametric protocol-driven agents and their integration in JADE. In: Proceedings of CILC (2015)

    Google Scholar 

  20. Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., Ziuziański, P.: Artificial intelligence and multi-agent software for e-health knowledge management system. Informatyka Ekonomiczna - Business Informatics 2(32), 51–62 (2014)

    Google Scholar 

  21. Hindriks, K.V., de Boer, F.S., van der Hoek, W., Meyer, J.C.: Agent programming in 3APL. Auton. Agents Multi Agent Syst. 2(4), 357–401 (1999)

    Article  Google Scholar 

  22. Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. Knowl. Eng. Rev. 19(4), 281–316 (2005)

    Article  Google Scholar 

  23. Isern, D., Moreno, A.: A systematic literature review of agents applied in healthcare. J. Med. Syst. 40(2), 43: 1–43: 14 (2016)

    Article  Google Scholar 

  24. Jennings, N.R., Sycara, K.P., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi Agent Syst. 1(1), 7–38 (1998)

    Article  Google Scholar 

  25. Jung, Y., Kim, M., Masoumzadeh, A., Joshi, J.B.: A survey of security issue in multi-agent systems. Artif. Intell. Rev. 37(3), 239–260 (2012)

    Article  Google Scholar 

  26. Korstanje, R., Brom, C., Gemrot, J., Hindriks, K.V.: A comparative study of programming agents in POSH and GOAL. In: Proceedings of ICAART 2016, pp. 192–203. SciTePress (2016)

    Google Scholar 

  27. Lesser, V., Ortiz Jr., C.L., Tambe, M.: Distributed sensor networks: a multiagent perspective, vol. 9. Springer Science & Business Media (2012)

    Google Scholar 

  28. Mascardi, V., Ancona, D.: Attribute global types for dynamic checking of protocols in logic-based multiagent systems. TPLP 13(4-5-Online-Supplement) (2013)

    Google Scholar 

  29. Meystre, S.: The current state of telemonitoring: a comment on the literature. Telemed. J. e Health 11(1), 63–69 (2005)

    Article  Google Scholar 

  30. Wight, N., Marinelli, K.A.: ABM clinical protocol #1: guidelines for blood glucose monitoring and treatment of hypoglycemia in term and late-preterm neonates. Breastfeed. Med. 1(3), 178–184 (2006)

    Article  Google Scholar 

  31. Nunes, I., Choren, R., Nunes, C., Fábri, B., Silva, F., de Carvalho, G.R., de Lucena, C.J.P.: Supporting prenatal care in the public healthcare system in a newly industrialized country. In: Proceedings of AAMAS 2010, pp. 1723–1730 (2010)

    Google Scholar 

  32. Rosaci, D., Sarné, G.M., Garruzzo, S.: Integrating trust measures in multiagent systems. Int. J. Intell. Syst. 27(1), 1–15 (2012)

    Article  Google Scholar 

  33. Sangiorgi, D.: On the origins of bisimulation, coinduction. ACM Trans. Program. Lang. Syst. 31(4), 15: 1–15: 41 (2009)

    Article  MATH  Google Scholar 

  34. Schwaibold, M., Gmelin, M., von Wagner, G., Schöchlin, J., Bolz, A.: Key factors for personal health monitoring and diagnosis device. In: Mobile Computing in Medicine, vol. 15 of LNI, pp. 143–150. GI (2002)

    Google Scholar 

  35. Shakshuki, E.M., Reid, M.: Multi-agent system applications in healthcare: current technology and future roadmap. In: Proceedings of ANT 2015, vol. 52 of Procedia Computer Science, pp. 252–261. Elsevier (2015)

    Google Scholar 

  36. Smith, B., Pisanelli, D.M., Gangemi, A., Stefanelli, M.: Clinical guidelines as plans - an ontological theory. Methods Inf. Med. 45(2), 204–210 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DIBRIS, University of Genova, Genoa, Italy

    Angelo Ferrando, Davide Ancona & Viviana Mascardi

Authors
  1. Angelo Ferrando
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davide Ancona
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Viviana Mascardi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Angelo Ferrando .

Editor information

Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. TU Clausthal, Clausthal-Zellerfeld, Niedersachsen, Germany

    Jörg P. Müller

  3. Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Ingrid Nunes

  4. University of Texas at Dallas, Richardson, Texas, USA

    Rym Zalila-Wenkstern

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Ferrando, A., Ancona, D., Mascardi, V. (2016). Monitoring Patients with Hypoglycemia Using Self-adaptive Protocol-Driven Agents: A Case Study. In: Baldoni, M., Müller, J., Nunes, I., Zalila-Wenkstern, R. (eds) Engineering Multi-Agent Systems. EMAS 2016. Lecture Notes in Computer Science(), vol 10093. Springer, Cham. https://doi.org/10.1007/978-3-319-50983-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50983-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50982-2

  • Online ISBN: 978-3-319-50983-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation