Preventing Elderly from Falls: The Agent Perspective in EPRs

  • Sebastian Ahrndt
  • Johannes Fähndrich
  • Sahin Albayrak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7879)


This work presents an approach combining multiple electronic patient records (EPR) to a self-learning fall risk assessment tool. We utilized the agent-perspective to model the system, to address privacy issues and to evaluate different distributed information fusion and opinion aggregation techniques towards there applicability to the addressed domain. Each agent represents a single patient negotiating about unknown fall risk influences in order to adapt the fall-risk assessment tool to the population under care. In addition, we will outline the planned real-world case study.


Fall Risk Electronic Patient Record Information Fusion Group Opinion Agent Perspective 
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  1. 1.
    Ahrndt, S., Rieger, A., Albayrak, S.: Entwicklung einer mobilen elektronischen Patientenakte für die ambulante Versorgung in ländlichen Regionen (development of a mobile electronic patient record for ambulatory health care in the countryside). In: Goltz, U., Magnor, M., Appelrath, H.J., Matthies, H., Balke, W.T., Wolf, L. (eds.) INFORMATIK 2012. Lecture Notes in Informatics, vol. (208), pp. 1167–1181. Gesellschaft für Informatik, Braunschweig (2012)Google Scholar
  2. 2.
    Bernardo, J.M., Smith, A.F.M.: Bayesian theory. Wiley series in probability and statistics, Wiley, Chichester [u.a.], repr. edn.(2004),
  3. 3.
    Cooke, R.: Experts in Uncertainty: Opinion and Subjective Probability in Science. Oxford University Press (January 1991)Google Scholar
  4. 4.
    Fähndrich, J.: Analyse von Verfahren zur Kombination von Expertenwissen in Form von Wahrscheinlichkeitsverteilungen im Hinblick auf die verteilte lokale Bayes’sche Fusion. Diploma thesis, Karlsruhe Institut of Technology (May 2010)Google Scholar
  5. 5.
    Fiss, T., Dreier, A., Meinke, C., van den Berg, N., Ritter, C.A., Hoffmann, W.: Frequency of inappropriate drugs in primary care: Analysis of a sample of immobile patients who received periodic home visits. Age and Ageing 40(1), 66–73 (2010)CrossRefGoogle Scholar
  6. 6.
    Fiss, T., Ritter, C.A., Alte, D., van den Berg, N., Hoffmann, W.: Detection of drug related problems in an interdisciplinary health care model for rural areas in germany. Pham. World Sci. 32(5), 566–574 (2010)CrossRefGoogle Scholar
  7. 7.
    Genest, C.: Pooling operators with the marginalization property. The Canadian Journal of Statistics/La Revue Canadienne de Statistique 12(2), 153–163 (1984), MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Genest, C., McConway, K.K., Schervish, M.M.: Characterization of externally bayesian pooling operators. The Annals of Statistics 14(2), 487–501 (1986), MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Genest, C., Weerahandi, S., Zidek, J.: Aggregating opinions through logarithmic pooling. Theory and Decision 17, 61–70 (1984), MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Goth, G.: Analyzing medical data. Communications of the ACM 55(6), 13–15 (2012)CrossRefGoogle Scholar
  11. 11.
    Halpern, J.: From statistical knowledge bases to degrees of belief: an overview. In: Proceedings of the Twenty-Fifth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, p. 113. ACM (2006),
  12. 12.
    Hirsch, B., Konnerth, T., Heßler, A.: Merging agents and services – the JIAC agent platform. In: Bordini, R.H., Dastani, M., Dix, J., Amal, E.F.S. (eds.) Multi-Agent Programming: Languages, Tools and Applications, pp. 159–185. Springer (2009)Google Scholar
  13. 13.
    Hoffmann, W., van den Berg, N., Thyrian, J.R., Fiss, T.: Frequency and determinants of potential drug-drug interactions in an elderly population receiving regular home visits by GPs – results of the home medication review in the AGnES-studies. Pharmacoepidemiology and Drug Safety 20(12), 1311–1318 (2011)CrossRefGoogle Scholar
  14. 14.
    König, H.H.: Gesundheitsökonomische Aspekte der Sturz- und Frakturprävention (2012), (last visit: October 29, 2012)
  15. 15.
    Lindley, D.: Theory and practice of bayesian statistics. The Statistician 32(1), 1–11 (1983), CrossRefGoogle Scholar
  16. 16.
    Mohomed, I., Misra, A., Ebling, M., Jerome, W.: HARMONI: Context-aware filtering of sensor data for continuous remote health monitoring. In: Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2008), pp. 248–251. IEEE Computer Society (March 2008)Google Scholar
  17. 17.
    Moulton, B., Chaczko, Z., Karatovic, M.: Data fusion and aggregation methods for pre-processing ambulatory monitoring and remote sensor data for upload to personal electronic health records. International Journal of Digital Content Technology and its Applications 3(4), 120–127 (2009)Google Scholar
  18. 18.
    Pennock, D., Wellman, M.: Graphical models for groups: Belief aggregation and risk sharing. Decision Analysis 2(3), 148–164 (2005), CrossRefGoogle Scholar
  19. 19.
    Roback, P.J., Givens, G.H.: Supra-bayesian pooling of priors linked by a deterministic simulation model. Communications in Statistics – Simulation and Computation 30, 447–476 (2007)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Rubenstein, L.Z., Josephson, K.: Falls and their prevention in elderly people: What does the evidence show? Med. Clin. North Am. 90(5), 807–824 (2006)CrossRefGoogle Scholar
  21. 21.
    Schenk, A., Ahrndt, S., Albayrak, S.: Predicting fall risks in electronic patient records. In: Goltz, U., Magnor, M., Appelrath, H.J., Matthies, H., Balke, W.T., Wolf, L. (eds.) INFORMATIK 2012. Lecture Notes in Informatics, vol. (208), pp. 1194–1198. Gesellschaft für Informatik, Braunschweig (2012)Google Scholar
  22. 22.
    Torra, V., Narukawa, Y.: Modeling decisions: information fusion and aggregation operators. Springer (2007)Google Scholar
  23. 23.
    Wark, E.B.J.S.: Concepts, Models, and Tools for Information Fusion. Artech House (2007),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sebastian Ahrndt
    • 1
  • Johannes Fähndrich
    • 1
  • Sahin Albayrak
    • 1
  1. 1.DAI-LaborTechnische Universität BerlinBerlinGermany

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