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Monitoring in the Healthcare Setting

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Foundations of Biomedical Knowledge Representation

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Notes

  1. 1.

    The latter acception is typically employed when the model of the system carries a normative meaning.

  2. 2.

    http://runtime-verification.org/.

References

  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)

    Article  Google Scholar 

  2. van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement. Springer, Heidelberg (2011)

    Book  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  6. Bright, T.J., et al.: Effect of clinical decision-support systems a systematic review. Ann. Intern. Med. 157, 29–43 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Cismondi, F.C., et al.: Reducing ICU blood draws with artificial intelligence. Crit. Care 16, 436 (2012)

    Article  Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. Hanson III, C.W., Marshall, B.E.: Artificial intelligence applications in the intensive care unit. Crit. Care Med. 29, 427 (2001)

    Article  Google Scholar 

  19. Knaus, W.A., et al.: APACHE II: a severity of disease classification system. Crit. Care Med. 13, 818–829 (1985)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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/70156/2010

    Google Scholar 

  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)

    Chapter  Google Scholar 

  25. Ramon, J., et al.: Mining data from intensive care patients. Adv. Eng. Inform. 21, 243–256 (2007)

    Article  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Federico Chesani

  2. National University of Ireland, Galway, Ireland

    Catherine G. Enright & Michael G. Madden

  3. KRDB Research Centre, Free University of Bozen-Bolzano, Bolzano, Italy

    Marco Montali

Authors
  1. Federico Chesani
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  2. Catherine G. Enright
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  3. Marco Montali
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  4. Michael G. Madden
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Corresponding author

Correspondence to Federico Chesani .

Editor information

Editors and Affiliations

  1. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, The Netherlands

    Arjen Hommersom

  2. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, Gelderland, The Netherlands

    Peter J.F. Lucas

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Chesani, F., Enright, C.G., Montali, M., Madden, M.G. (2015). Monitoring in the Healthcare Setting. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-28007-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28006-6

  • Online ISBN: 978-3-319-28007-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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