Verdaguer A, Patak A, Sancho J J, Sierra C, Sanz F
Departament d'Informàtica Biomèdica, Facultat de Medicina (Universitat Autònoma de Barcelona), Spain.
Comput Biomed Res. 1992 Dec;25(6):511-26. doi: 10.1016/0010-4809(92)90006-v.
The present study validates the expert system PNEUMON-IA. The aim of PNEUMON-IA is assessing the etiology of community-acquired pneumonias from clinical, radiological, and laboratory data obtained at the onset of the disease. Validation was performed using data from medical records of 76 patients with proven clinical diagnosis of pneumonia. The etiological diagnoses provided by PNEUMON-IA were compared to those established by five specialists unrelated to the development of the expert system. For each etiological possibility, both PNEUMON-IA and the experts provided a causal possibility, expressed as a linguistic label (i.e., "almost impossible"). Linguistic labels were then converted to numeric values. In the majority of cases, an etiological diagnosis was unavailable to be used as a gold standard. To overcome this limitation, distances between arrays of etiological possibilities given by specialists and by PNEUMON-IA were considered as an agreement measure between diagnoses. Cluster analysis based on those distances was used to classify PNEUMON-IA among experts. Results showed the same differences between specialists and PNEUMON-IA as among the specialists themselves. The method used to validate PNEUMON-IA could prove useful to assess the performance of expert systems in fields where no gold standard is available.
本研究对专家系统PNEUMON - IA进行了验证。PNEUMON - IA的目的是根据疾病发作时获得的临床、放射学和实验室数据评估社区获得性肺炎的病因。验证使用了76例经临床确诊为肺炎患者的病历数据。将PNEUMON - IA提供的病因诊断与由与专家系统开发无关的五位专家确定的诊断进行比较。对于每种病因可能性，PNEUMON - IA和专家都给出了一种因果可能性，以语言标签表示（即“几乎不可能”）。然后将语言标签转换为数值。在大多数情况下，无法获得病因诊断作为金标准。为克服这一局限性，将专家和PNEUMON - IA给出的病因可能性数组之间的距离视为诊断之间的一致性度量。基于这些距离的聚类分析用于在专家中对PNEUMON - IA进行分类。结果显示，专家与PNEUMON - IA之间的差异与专家自身之间的差异相同。用于验证PNEUMON - IA的方法可能被证明有助于评估在没有金标准的领域中专家系统的性能。
