Respiratory and Critical Care Group (RCARE), Center for Model-based Medical Decision Support, Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7E, DK-9220 Aalborg East, Denmark.
Comput Methods Programs Biomed. 2013 Jun;110(3):361-8. doi: 10.1016/j.cmpb.2013.02.001. Epub 2013 Mar 5.
Diagnosis and classification of chronic obstructive pulmonary disease (COPD) may be seen as difficult. Causal reasoning can be used to relate clinical measurements with radiological representation of COPD phenotypes airways disease and emphysema. In this paper a causal probabilistic network was constructed that uses clinically available measurements to classify patients suffering from COPD into the main phenotypes airways disease and emphysema. The network grades the severity of disease and for emphysematous COPD, the type of bullae and its location central or peripheral. In four patient cases the network was shown to reach the same conclusion as was gained from the patients' High Resolution Computed Tomography (HRCT) scans. These were: airways disease, emphysema with central small bullae, emphysema with central large bullae, and emphysema with peripheral bullae. The approach may be promising in targeting HRCT in COPD patients, assessing phenotypes of the disease and monitoring its progression using clinical data.
慢性阻塞性肺疾病（COPD）的诊断和分类可能被认为是困难的。因果推理可用于将临床测量值与 COPD 表型气道疾病和肺气肿的放射学表现联系起来。在本文中，构建了一个因果概率网络，该网络使用临床可用的测量值将患有 COPD 的患者分为主要表型气道疾病和肺气肿。该网络对疾病的严重程度进行分级，对于气肿性 COPD，还对大疱的类型及其位置（中央或外周）进行分级。在四个患者病例中，网络得出的结论与患者的高分辨率计算机断层扫描（HRCT）扫描结果相同。这些病例包括：气道疾病、中央小疱肺气肿、中央大疱肺气肿和外周疱肺气肿。该方法有望用于针对 COPD 患者的 HRCT，使用临床数据评估疾病表型并监测其进展。
