The use of knowledge-based systems to improve medical knowledge about urine analysis

Abstract Urine protein diagnostics has developed into a routine method for screening and monitoring kidney diseases. It is based on the quantitative measurement of total protein, albumin, α 1 -microglobulin, immunoglobulin G and α 2 -macroglobulin (all related to urine creatinine), as well as a dipstick screening. The excretion pattern of the marker proteins allows differentiation of haematuria, leukocyturia and proteinuria and to assign them to prerenal, renal and postrenal causes. In order to provide the clinical partner not only with pure analytical results, but to support clinical decision making by an interpretative report, a urine protein expert system (UPES) has been developed. Based on a database containing more than 500 excretion patterns of patients with known diagnoses, a knowledge base was extracted. In its modules plausibility control, glomerular filtration rate, hematuria, leukocyturia and proteinuria , IF–THEN-rules interpret the given patterns and select matching text elements. The knowledge base has been integrated in the modern expert system shell WILAS, and the resulting interpretation system has been thoroughly verified and validated. An internal acceptance study revealed that urine protein differentiation is widely accepted as a diagnostic option and that its interpretation, provided with the help of UPES, is appreciated as a service. In an external study, the usability of UPES in routine and its knowledge representation was evaluated in 11 centres consisting of laboratories and nephrological partners. Over seven months, more than 500 cases were interpreted using UPES and documented by questionnaires. The discussion of the results at a user conference revealed that the problem of analytical standardisation as well as the common definition of diagnostic terms by laboratory staff and clinicians play a crucial role for the use of knowledge-based systems in laboratory medicine. Whereas the user interface of UPES was judged very heterogeneously, the correctness of the proposed interpretations was unanimously rated as “good”. As a result of the evaluation, the user interface has been modernised. The knowledge base has been extended to address paediatric issues as well, and to take clinical information and previous findings into consideration.