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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 >Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support
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Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support

机译：概率和模糊认知方法在医学决策支持语义网框架中的应用

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?Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support.?Both approaches were implemented in semantic web framework.?For reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web.?To assess the formal models' performance, the UTI therapy problem was selected as a proof of concept example.?The validation results showed that using probabilistic networks and fuzzy cognitive maps in semantic web framework is reliable and efficient for decision support tasks. This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI.
机译：将贝叶斯信念网络（BBNs）和模糊认知图（FCMs）作为动态影响图，用于处理医学知识形式化任务以提供决策支持。在语义Web框架中实现了这两种方法。在语义网上开发了通用模型推理引擎EYE模型，并带有必要的插件。为了评估形式模型的性能，选择了UTI治疗问题作为概念验证的例子。验证结果表明在语义Web框架中使用概率网络和模糊认知图对于决策支持任务是可靠且高效的。这项研究的重点是使用语义网中实现的概率和模糊影响过程进行医学知识表示和推理，以用于决策支持任务。作为动态影响图的贝叶斯信念网络（BBN）和模糊认知图（FCM）被用于处理医学知识形式化任务以提供决策支持。为了在这些知识模型上执行推理，在语义Web中开发了带有必要插件的通用推理引擎EYE。这两种形式的方法构成了提议的决策支持系统（DSS），旨在识别医疗问题的适当准则，并提出易于理解的行动指南来指导从业人员。选择尿路感染（UTI）问题作为概念验证示例，以检查在语义网中实现的拟议形式化技术。将UTI治疗的医学指南正式化为BBN和FCM知识模型。为了评估正式模型的性能，从数据库中提取了55例患者病例并进行了分析。结果表明，所提出的方法在语义网中有效地正规化了医学知识，并为抗生素对UTI的建议做出了前端决策。

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