Ivandić M, Hofmann W, Guder W G
Institut für Klinische Chemie, Städt. Krankenhaus München-Bogenhausen, Germany.
Clin Chem. 1996 Aug;42(8 Pt 1):1214-22.
Based on the quantitative determination of creatinine, total protein, albumin, alpha 1-microglobulin, IgG, alpha 2-macroglobulin, and N-acetyl-beta, D-glucosaminidase in urine in combination with a test strip screening, the findings of hematuria, leukocyturia, and proteinuria can be assigned to prerenal, renal, or postrenal causes. Using this graded diagnostic strategy as a knowledge base, we developed a computerbased expert system for urine protein differentiation ("UPES") as a decision-supporting tool. The knowledge base was implemented as a combination of "if/then" rules and two-step bivariate distance classification of marker proteins. The knowledge for this form of pattern recognition was derived from the results for a set of 267 patients with clinically and histologically documented nephropathies. To determine the diagnostic value of UPES, we tested another set of data: results for 129 urine analyses from 94 patients. Using these data, the system reached 98% concordance with the clinical diagnoses for the patients and was superior to the diagnostic interpretations of four human experts. UPES has been successfully integrated into the laboratory routine process, including automated data import.
基于对尿液中肌酐、总蛋白、白蛋白、α1 -微球蛋白、IgG、α2 -巨球蛋白和N -乙酰-β - D -氨基葡萄糖苷酶的定量测定，并结合试纸条筛查，血尿、白细胞尿和蛋白尿的检查结果可归因于肾前性、肾性或肾后性病因。以这种分级诊断策略作为知识库，我们开发了一个基于计算机的尿液蛋白鉴别专家系统（“UPES”）作为决策支持工具。知识库通过“if/then”规则和标记蛋白的两步双变量距离分类相结合来实现。这种模式识别形式的知识来源于一组267例有临床和组织学记录的肾病患者的结果。为了确定UPES的诊断价值，我们测试了另一组数据：94例患者的129次尿液分析结果。利用这些数据，该系统与患者的临床诊断达成了98%的一致性，并且优于四位人类专家的诊断解释。UPES已成功整合到实验室常规流程中，包括自动数据导入。
