SOC: A distributed decision support architecture for clinical diagnosis

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Abstract

In this paper we introduce SOC (Sistema de Orientatión Clínica, Clinic Orientation System), a novel distributed decision support system for clinical diagnosis. The decision support systems are based on pattern recognition engines which solve different and specific classification problems. SOC is based on a distributed architecture with three specialized nodes: 1) Information System where the remote data is stored, 2) Decision Support Webservices which contains the developed pattern recognition engines and 3) Visual Interface, the clinicians' point of access to local and remote data, statistical anasysis tools and distributed information. A location-independent and multi-platform system has been developed to bring together hospitals and institutions to research useful tools in clinical and laboratory environments. The nodes maintenance and upgrade are automatically controlled by the architecture. Two examples of the application of SOC are presented. The first example is the Soft Tissue Tumors (STT) diagnosis. The decision support systems are based on pattern recognition engines to classify between benign/malignant character and histological groups with good estimated efficiency. In the second example we present clinical support for Microcytic Anemia (MA) diagnosis. For this task, the decision support systems are based, too, on pattern recognition engines to classify between normal, ferropenic anemia and thalassemia. This tool will be useful for several puposes: to assist the radiologist/hematologist decision in a new case and help the education of new radiologist/hematologist without expertise in STT or MA diagnosis. © Springer-Verlag Berlin Heidelberg 2004.

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APA

Vicente, J., Garcia-Gomez, J. M., Vidal, C., Marti-Bonmati, L., Del Arco, A., & Robles, M. (2004). SOC: A distributed decision support architecture for clinical diagnosis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3337, 96–104. https://doi.org/10.1007/978-3-540-30547-7_11

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