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SOC: A Distributed Decision Support Architecture for Clinical Diagnosis

机译:SOC:用于临床诊断的分布式决策支持架构

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In this paper we introduce SOC (Sistema de Orientación 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 Web-services 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.
机译:本文介绍了SoC(Sistema deOrientaciónClínica,临床方向系统),这是一种用于临床诊断的新型分布式决策支持系统。决策支持系统基于模式识别引擎,该发动机解决了不同和特定的分类问题。 SOC基于具有三个专业节点的分布式架构:1)信息系统,其中远程数据存储,2)决策支持网络服务,其中包含开发的模式识别引擎和3)视觉界面,临床医生访问本地和远程数据,统计又分析工具和分布式信息。已经开发了一个独立于独立的和多平台系统,以汇集医院和机构,在临床和实验室环境中研究有用的工具。节点维护和升级由架构自动控制。提出了SOC应用的两个例子。第一个例子是软组织肿瘤(STT)诊断。决策支持系统基于模式识别发动机,以分类良性/恶性特征和组织学团,具有良好的估计效率。在第二个例子中,我们呈现对微细血症贫血(MA)诊断的临床支持。对于此任务,决策支持系统也基于模式识别发动机,以分类正常,脱盐性贫血和地中海贫血之间。该工具对几个蛹有用:在新案例中协助放射科医师/血液学学者的决定,并帮助新放射科医生/血液学家的教育,无需STT或MA诊断。

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