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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Cross-domain probabilistic inference in a clinical decision support system: examples for dermatology and rheumatology.
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Cross-domain probabilistic inference in a clinical decision support system: examples for dermatology and rheumatology.

机译：临床决策支持系统中的跨域概率推断：皮肤病和风湿病的示例。

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INTRODUCTION: Maintaining a large diagnostic knowledge base (KB) is a demanding task for any person or organization. Future clinical decision support system (CDSS) may rely on multiple, smaller and more focused KBs developed and maintained at different locations that work together seamlessly. A cross-domain inference tool has great clinical import and utility. METHODS: We developed a modified multi-membership Bayes formulation to facilitate the cross-domain probabilistic inferencing among KBs with overlapping diseases. Two KBs developed for evaluation were non-infectious generalized blistering diseases (GBD) and autoimmune diseases (AID). After the KBs were finalized, they were evaluated separately for validity. RESULT: Ten cases from medical journal case reports were used to evaluate this "cross-domain" inference across the two KBs. The resultant non-error rate (NER) was 90%, and the average of probabilities assigned to the correct diagnosis (AVP) was 64.8% for cross-domain consultations. CONCLUSION: A novel formulation is now available to deal with problems occurring in a clinical diagnostic decision support system with multi-domain KBs. The utilization of this formulation will help in the development of more integrated KBs with greater focused knowledge domains.
机译：简介：维护大型诊断知识库（KB）对任何个人或组织都是一项艰巨的任务。未来的临床决策支持系统（CDSS）可能依赖于在不同位置无缝开发并维护的多个，更小，更集中的知识库。跨域推理工具具有很大的临床意义和实用性。方法：我们开发了一种改进的多成员贝叶斯公式，以促进具有重叠疾病的知识库之间的跨域概率推断。为评估而开发的两个知识库分别是非感染性泛发性水疱病（GBD）和自身免疫性疾病（AID）。知识库最终确定后，将分别评估其有效性。结果：医学期刊病例报告中的10个病例用于评估两个KB之间的“跨域”推断。最终的无错误率（NER）为90％，跨域咨询的平均分配给正确诊断（AVP）的概率为64.8％。结论：现在可以使用一种新颖的配方来解决在具有多域知识库的临床诊断决策支持系统中出现的问题。利用此公式将有助于开发具有更集中的知识领域的集成度更高的知识库。

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