A RESTful Approach for Developing Medical Decision Support Systems

  • Tobias WellerEmail author
  • Maria Maleshkova
  • Keno März
  • Lena Maier-Hein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9341)


Current developments in the medical sector are witnessing the growing digitalization of data in terms of patient tests, records and trials, use of sensors for monitoring and recording procedures, and employing digital imagery. Besides the increasing number of published guidelines and studies, it has been shown that clinicians are often unable to observe these guidelines correctly during the actual care process. [1] The increasing number of guidelines and studies, and also the fact that physicians are often unable to observe these guidelines correctly provide the foundation for this paper. We will tackle these problems by developing a medical assistance system which processes the gathered and integrated data from different sources, and assists the physicians in making decisions, preparing treatment plans, and even guide surgeons during invasive procedures. In this paper we demonstrate how a RESTful architecture combined with applying Linked Data principles for data storage and exchange can effectively be used for developing medical decision support systems. We propose different autonomous subsystems that automatically process data relevant to their purpose. These so-called “Cognitive Apps” provide RESTful interfaces and perform tasks such as converting and uploading data and deducing medical knowledge by using inference rules. The result is an adaptive decision support system, based on distributed decoupled Cognitive Apps, which can preprocess data in advance but also support real-time scenarios. We demonstrate the practical applicability of our approach by providing an implementation of a system for processing patients with liver tumors. Finally, we evaluate the system in terms of knowledge deduction and performance.



This work was carried out with the support of the German Research Foundation (DFG) as part of project A02, I01, and S01, SFB/TRR 125 Cognition-Guided Surgery. We would particularly like to thank Patrick Philipp, Mohammadreza Hafezi, Arianeb Mehrabi and Marco Nolden. All of the authors state no conflict of interests. All studies have been approved and performed in accordance with ethical standards. Patient data were gathered and evaluated under informed consent only.


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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Tobias Weller
    • 1
    Email author
  • Maria Maleshkova
    • 1
  • Keno März
    • 2
  • Lena Maier-Hein
    • 2
  1. 1.AIFB, Karlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.German Cancer Research CenterHeidelbergGermany

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