gms | German Medical Science

GMDS 2012: 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

16. - 20.09.2012, Braunschweig

eHealthMonitor – platform that creates a Personalized eHealth Knowledge Space (PeKS)

Meeting Abstract

Suche in Medline nach

  • Manfred Criegee-Rieck - Universität Erlangen, Deutschland
  • Hans-Ulrich Prokosch - Universität Erlangen, Deutschland
  • Martin Sedlmayr - Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Deutschland

GMDS 2012. 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Braunschweig, 16.-20.09.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. Doc12gmds062

doi: 10.3205/12gmds062, urn:nbn:de:0183-12gmds0625

Veröffentlicht: 13. September 2012

© 2012 Criegee-Rieck et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen ( Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.



Motivation: Progress in Medical Informatics depends on procedures for the qualified linkage between medical knowledge and patient data [1]. Just as citizens increasingly search and use health information from the internet [2]. Indeed, these Internet health services are manifold and comprehensive but too generic and usually not tailored to individual conditions and settings. To improve this limitation in personalized functionalities and problem-oriented knowledge acquisition e.g. to improve usefulness of internet health services, individualization has to be done manually with the disadvantage that this process is time-consuming, labor-intensive and defective. Thus, the EU funded multinational project eHealthMonitor (eHM) has its research focus on a problem-based Personal electronic Knowledge Space (PeKS). To address the outlined challenges we use semantic methods of modeling domain knowledge combined with multi-agent technology for the purpose of automated content aggregation with decision and monitoring support services. This strategy should enable the vision of context sensitive knowledge-on-demand health services.

Scope and Settings: Topically dedicated and identified were three healthcare scenarios to evaluate the eHM platform and the integrated health services:

  • The first scenario covers all stakeholders and their information requirements around Dementia. Three German project partners research this medical domain as well as the semantic reasoning processes and practices. The relevant stakeholders and consumers of the individualized knowledge space are e.g. citizens with risk to suffer at Dementia, patients with mild cognitive impairment, family caregivers, professional caregivers and relatives.
  • The second scenario centers on patients with cardiovascular diseases with the specific challenge to incorporate data from wearable medical devices into the PeKS.
  • The third scenario has the perspective of an insurance company with the focus on risk reduction and prevention on patients who suffer or have the risk for chronic diseases.

Despite the indicated shortcomings, all three medical domains have a high attentiveness and a significant economically footprint in most European healthcare systems. As a cross-country project eHealthMonitor provides a topically tailored PeKS which aggregates all relevant knowledge and information sources (e.g. PHR , EHR, internet content sites, mobile sensors, etc.) to improve cooperation and shared decision making between all stakeholders(e.g. patients, doctors, caregivers, relatives).

Solution and Methods: A service-oriented architecture interconnected with mobile device technologies enables and eases the access to the personal knowledge space. Semantic web technologies combined with multi-agent systems [3] based on the belief-desire-intention principle are an innovative approach to progress the required personalization process. They generate a Personal eHealth Knowledge Space in compliance with patient guidance needs. Patient needs are e.g. the context-sensitive selection and presentation as well as adaption to personal literacy levels which depend on age, education or even ethnic background.

Challenges for Medical Informatics in this project are the integration of semantic modeling and reasoning principles into a multi-agent framework with problem solving abilities again linked with state-of the-art medical and public-health information process-management. In addition, the requirement analysis processes which describe the eHM services in a problem-based and domain specific way, in order to comply with user expectations are one of the challenging tasks in this project.


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