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Web-tool to Support Medical Experts in Probabilistic Modelling Using Large Bayesian Networks With an Example of Hinosinusitis

机译：使用大型贝叶斯网络支持概率模型中的医学专家，其中大型贝叶斯网络具有产后炎的概率模型

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For many complex diseases, finding the best patient-specific treatment decision is difficult for physicians due to limited mental capacity. Clinical decision support systems based on Bayesian networks (BN) can provide a probabilistic graphical model integrating all necessary aspects relevant for decision making. Such models are often manually created by clinical experts. The modeling process consists of graphical modeling conducted by collecting of information entities, and probabilistic modeling achieved through defining the relations of information entities to their direct causes. Such expert-based probabilistic modelling with BNs is very time intensive and requires knowledge about the underlying modeling method. We introduce in this paper an intuitive web-based system for helping medical experts generate decision models based on BNs. Using the tool, no special knowledge about the underlying model or BN is necessary. We tested the tool with an example of modeling treatment decisions of Rhinosinusitis and studied its usability.
机译：对于许多复杂的疾病，由于心理能力有限，医生难以找到最佳的患者特异性治疗决策。基于贝叶斯网络（BN）的临床决策支持系统可以提供概率图形模型，整合了与决策相关的所有必要方面。这些模型通常由临床专家手动创建。建模过程包括通过收集信息实体进行的图形建模，并且通过将信息实体的关系定义到其直接原因来实现概率建模。基于专家的概率建模与BNS非常密集，需要了解底层建模方法。我们在本文中介绍了一个直观的基于网络的系统，用于帮助医学专家基于BNS生成决策模型。使用该工具，不需要对底层模型或BN的特殊知识。我们用鼻窦炎的治疗决策的示例测试了该工具，并研究了其可用性。

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