Kahn C E, Roberts L M, Shaffer K A, Haddawy P
Department of Radiology, Medical College of Wisconsin, Milwaukee 53226, USA.
Comput Biol Med. 1997 Jan;27(1):19-29. doi: 10.1016/s0010-4825(96)00039-x.
Bayesian networks use the techniques of probability theory to reason under uncertainty, and have become an important formalism for medical decision support systems. We describe the development and validation of a Bayesian network (MammoNet) to assist in mammographic diagnosis of breast cancer. MammoNet integrates five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists to determine the probability of malignancy. We outline the methods and issues in the system's design, implementation, and evaluation. Bayesian networks provide a potentially useful tool for mammographic decision support.
贝叶斯网络运用概率论技术在不确定性情况下进行推理，已成为医学决策支持系统的一种重要形式。我们描述了一个用于辅助乳腺癌钼靶诊断的贝叶斯网络（MammoNet）的开发与验证过程。MammoNet整合了五个患者病史特征、两个体格检查结果以及由经验丰富的放射科医生提取的15个钼靶特征，以确定恶性肿瘤的概率。我们概述了该系统设计、实施和评估中的方法及问题。贝叶斯网络为钼靶决策支持提供了一个潜在的有用工具。
