Aronsky D, Haug P J
Dept. of Medical Informatics, LDS Hospital/University of Utah, Salt Lake City, USA.
Proc AMIA Symp. 1998:632-6.
We present the development and the evaluation of a Bayesian network for the diagnosis of community-acquired pneumonia. The Bayesian network is intended to be part of a larger decision support system which assists emergency room physicians in the management of pneumonia patients. Minimal data entry from the nurse or the physician, timely availability of clinical parameters, and high accuracy were requirements we tried to meet. Data from more than 32,000 emergency room patients over a period of 2 years (June 1995-June 1997) were extracted from the clinical information system to train and test the Bayesian network. The network performed well in discriminating patients with pneumonia from patients with other diseases. The Bayesian network achieved a sensitivity of 95%, a specificity of 96.5%, an area under the receiver operating characteristic of 0.98, and a predictive value positive of 26.8%. Our feasibility study demonstrates that the proposed Bayesian network is an appropriate method to detect pneumonia patients with high accuracy. The study suggests that the proposed Bayesian network may represent a successful component within a larger decision support system for the management of community-acquired pneumonia.
我们展示了用于社区获得性肺炎诊断的贝叶斯网络的开发与评估。该贝叶斯网络旨在成为一个更大的决策支持系统的一部分，该系统可协助急诊室医生管理肺炎患者。我们试图满足的要求包括护士或医生的最少数据录入、临床参数的及时获取以及高准确性。从临床信息系统中提取了两年（1995年6月至1997年6月）期间超过32,000名急诊室患者的数据，用于训练和测试贝叶斯网络。该网络在区分肺炎患者与其他疾病患者方面表现良好。贝叶斯网络的灵敏度达到95%，特异度为96.5%，受试者操作特征曲线下面积为0.98，阳性预测值为26.8%。我们的可行性研究表明，所提出的贝叶斯网络是一种高精度检测肺炎患者的合适方法。该研究表明，所提出的贝叶斯网络可能是社区获得性肺炎管理的更大决策支持系统中的一个成功组成部分。
