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The Research of Clinical Decision Support System Based on Three-Layer Knowledge Base Model

机译:基于三层知识库模型的临床决策支持系统研究

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摘要

In many clinical decision support systems, a two-layer knowledge base model (disease-symptom) of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property) to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.
机译:在许多临床决策支持系统中,使用了规则推理的两层知识库模型(疾病 - 症状)。这种模型往往并不表达知识,因为它只是患有某些症状存在的疾病。在这项研究中,我们提出了一种三层知识库模型(疾病 - 症状 - 属性)来利用更多有用的信息。该系统迭代地计算可能患有基于多主生物幼稚贝叶斯算法的疾病的患者的概率,其中这些疾病症状的特异性因诊断疾病的贡献程度的估算而加权。它显着降低了属性之间的依赖关系,以更好地应用Naive Bayes算法。然后,完成了推理引擎参数优化的在线学习过程。最后,我们的决策支持系统利用三层模型由两位经验丰富的医生正式评估。通过预测结果与临床结果之间的比较,我们的系统可以向医生提供有效的临床建议。此外,我们发现,与双层模型相比,三层模型可以提高预测的准确性。鉴于本研究的一些局限性,我们还识别并讨论了几个需要继续改进的领域。

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