Advertisement

Monitoring in the Healthcare Setting

  • Federico Chesani
  • Catherine G. Enright
  • Marco Montali
  • Michael G. Madden
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9521)

Abstract

Monitoring is an activity in which a running system is observed, so as to become aware of its state. The fact that the system is observed makes monitoring complementary to approaches like formal verification and validation, which are tailored to assess the quality and trustworthiness of the system before the execution.

Keywords

Bayesian Network System Execution Runtime Verification Symbolic Event Healthcare Monitoring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    van der Aalst, W.M.P., et al.: Auditing 2.0: using process mining to support tomorrow’s auditor. IEEE Comput. 43(3), 90–93 (2010)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Aleks, N., et al.: Probabilistic detection of short events, with application to critical care monitoring. In: Proceedings of NIPS 2008: 22nd Annual Conference on Neural Information Processing Systems. Vancouver, Canada, pp. 49–56 (2008)Google Scholar
  4. 4.
    Barbini, E., et al.: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery part I: model planning. BMC Med. Inf. Decis. Making 7, 35 (2007)CrossRefGoogle Scholar
  5. 5.
    Bertoli, P., Dragoni, M., Ghidini, C., Martufi, E., Nori, M., Pistore, M., Di Francescomarino, C.: Modeling and monitoring business process execution. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 683–687. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Bright, T.J., et al.: Effect of clinical decision-support systems a systematic review. Ann. Intern. Med. 157, 29–43 (2012)CrossRefGoogle Scholar
  7. 7.
    Celi, L.A., et al.: An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study. Crit. Care 12(6), R151 (2008)CrossRefGoogle Scholar
  8. 8.
    Cevenini, G., et al.: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery part II: an illustrative example. BMC Med. Inform. Decis. Making 7, 36 (2007)CrossRefGoogle Scholar
  9. 9.
    Charitos, T., et al.: A dynamic bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Syst. Appl. 36(2), 1249–1258 (2009)CrossRefGoogle Scholar
  10. 10.
    Chase, J.G., et al.: Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice? Ann. Intensive Care 1, 11 (2011)CrossRefGoogle Scholar
  11. 11.
    Chatterjee, S., Russell, S.: Why are DBNs sparse? In: International Conference on Artificial Intelligence and Statistics, Sardinia, pp. 81–88 (2010)Google Scholar
  12. 12.
    Cismondi, F.C., et al.: Reducing ICU blood draws with artificial intelligence. Crit. Care 16, 436 (2012)CrossRefGoogle Scholar
  13. 13.
    Enright, C.G., Madden, M.G., Madden, N.: Bayesian networks for mathematical models: techniques for automatic construction and efficient inference. Int. J. Approximate Reasoning 54, 323–342 (2013)zbMATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Enright, C.G., Madden, M.G., Madden, N., Laffey, J.G.: Clinical time series data analysis using mathematical models and DBNs. In: Peleg, M., Lavrač, N., Combi, C. (eds.) AIME 2011. LNCS, vol. 6747, pp. 159–168. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Flores, J.M., et al.: Incorporating expert knowledge when learning bayesian network structure: a medical case study. Artif. Intell. Med. 53(3), 181–204 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    van Gerven, M.A.J., Taal, B.G., Lucas, P.J.F.: Dynamic bayesian networks as prognostic models for clinical patient management. J. Biomed. Inform. 41(4), 515–529 (2008)CrossRefGoogle Scholar
  17. 17.
    Ghezzi, C.: Evolution, adaptation, and the quest for incrementality. In: Calinescu, R., Garlan, D. (eds.) Monterey Workshop 2012. LNCS, vol. 7539, pp. 369–379. Springer, Heidelberg (2012)Google Scholar
  18. 18.
    Hanson III, C.W., Marshall, B.E.: Artificial intelligence applications in the intensive care unit. Crit. Care Med. 29, 427 (2001)CrossRefGoogle Scholar
  19. 19.
    Knaus, W.A., et al.: APACHE II: a severity of disease classification system. Crit. Care Med. 13, 818–829 (1985)CrossRefGoogle Scholar
  20. 20.
    Li, Q., Mark, R.G., Clifford, G.D.: Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator. BioMed. Eng. Online 8(1), 13 (2009)zbMATHCrossRefGoogle Scholar
  21. 21.
    Lucas, P.J.F., van der Gaag, L.C., Abu-Hanna, A.: Bayesian networks in biomedicine and health-care. Artif. Intell. Med. 30(3), 201–214 (2004)CrossRefGoogle Scholar
  22. 22.
    Ly, L.T., et al.: A framework for the systematic comparison and evaluation of compliance monitoring approaches. In: Gasevic, D., Hatala, M., Motahari Nezhad, H.R., Reichert, M. (eds.) Proceedings of the 17th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2013), pp. 7–16. IEEE (2013)Google Scholar
  23. 23.
    Portela, F., et al.: Knowledge discovery for pervasive and real-time intelligent decision support in intensive care medicine, 2011. Fundao para a Cincia e a Tecnologia (FCT) - PTDC/EIA/72819/ 2006, SFRH/BD/70156/2010Google Scholar
  24. 24.
    Radstake, N., Lucas, P.J.F., Velikova, M., Samulski, M.: Critiquing knowledge representation in medical image interpretation using structure learning. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds.) KR4HC 2010. LNCS, vol. 6512, pp. 56–69. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  25. 25.
    Ramon, J., et al.: Mining data from intensive care patients. Adv. Eng. Inform. 21, 243–256 (2007)CrossRefGoogle Scholar
  26. 26.
    Roberts, J.M., et al.: Bayesian networks for cardiovascular monitoring. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 205–209. IEEE Engineering in Medicine and Biology Society (2006)Google Scholar
  27. 27.
    Santos, M.F., Portela, F., Vilas-Boas, M.: INTCARE: multi-agent approach for real-time intelligent decision support in intensive medicine. Fundao para a Cincia e a Tecnologia (FCT) (2011)Google Scholar
  28. 28.
    Vens, C., Van Assche, A., Blockeel, H., Džeroski, S.: First order random forests with complex aggregates. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 323–340. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  29. 29.
    Zhang, Y., Szolovits, P.: Patient-specific learning in real time for adaptive monitoring in critical care. J. Biomed. Inform. 41(3), 452–460 (2008)CrossRefGoogle Scholar
  30. 30.
    Zhang, Y.: Predicting occurrences of acute hypoglycemia during insulin therapy in the intensive care unit. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBS 2008, pp. 3297–3300 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Federico Chesani
    • 1
  • Catherine G. Enright
    • 2
  • Marco Montali
    • 3
  • Michael G. Madden
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly
  2. 2.National University of IrelandGalwayIreland
  3. 3.KRDB Research CentreFree University of Bozen-BolzanoBolzanoItaly

Personalised recommendations