Haddawy P, Kahn C E, Butarbutar M
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee 53201.
Med Phys. 1994 Jul;21(7):1185-92. doi: 10.1118/1.597400.
Bayesian networks, a technique for reasoning under uncertainty, currently are being developed for application to medical decision making. To explore their usefulness for radiologic decision support, a Bayesian belief network was constructed in the domain of hepatobiliary disease. The network model's nodes represent diagnoses, physical findings, laboratory test results, and imaging study findings. The connections between nodes incorporate conditional probabilities, such as sensitivity and specificity, to represent probabilistic influences. Statistical data were abstracted from peer-reviewed journal articles on hepatobiliary disease, and a network was created to reflect the data. The network successfully determined the a priori probabilities of various diseases, and incorporated laboratory and imaging results to calculate the a posteriori probabilities. The most informative examination was identified, that is, the laboratory study or imaging procedure that led to the greatest diagnostic certainty. Bayesian networks represent a very promising technique for decision support in radiology: they can assist physicians in formulating diagnoses and in selecting imaging procedures.
贝叶斯网络是一种在不确定性情况下进行推理的技术，目前正被开发用于医学决策。为了探索其在放射学决策支持中的有用性，在肝胆疾病领域构建了一个贝叶斯信念网络。该网络模型的节点代表诊断、体格检查结果、实验室检查结果和影像学检查结果。节点之间的连接纳入了条件概率，如敏感性和特异性，以表示概率影响。统计数据摘自关于肝胆疾病的同行评审期刊文章，并创建了一个网络来反映这些数据。该网络成功地确定了各种疾病的先验概率，并结合实验室和影像学结果来计算后验概率。确定了最具信息量的检查，即导致最大诊断确定性的实验室研究或影像学检查。贝叶斯网络是放射学决策支持中一项非常有前景的技术：它们可以帮助医生制定诊断和选择影像学检查。
