Title: A RESTful approach for developing medical decision support systems
Authors: Weller, Tobias
Maleshkova, Maria 
März, Keno
Maier-Hein, Lena
Language: eng
Issue Date: 1-Jan-2015
Publisher: Springer
Document Type: Conference Object
Journal / Series / Working Paper (HSU): Lecture Notes in Computer Science
Volume: 9341
Page Start: 376
Page End: 384
Published in (Book): The Semantic Web: ESWC 2015 Satellite Events
Publisher Place: Berlin
Conference: ESWC 2015 Satellite Events ESWC 2015 Satellite Events, Portorož, Slovenia, May 31 – June 4, 2015
Abstract: 
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.
Organization Units (connected with the publication): Karlsruhe Institute of Technology
ISBN: 9783319256382
ISSN: 03029743
Publisher DOI: 10.1007/978-3-319-25639-9_50
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