PubMed · 2243551
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B Bergeron, S Locke. Speech recognition as a user interface.. https://pubmed.ncbi.nlm.nih.gov/2243551/
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Hepaxpert-I is an expert system that interprets the results of routine serologic tests for infection with hepatitis A or B virus. The tests measure antibody to the hepatitis A virus (anti-HAV), IgM antibody to the hepatitis A virus (IgM anti-HAV), hepatitis A virus (HAV) in the stool, hepatitis B surface antigen (HBsAg) and antibody (anti-HBs), antibody to hepatitis B core antigen (anti-HBc and IgM anti-HBc), and hepatitis B envelope antigen (HBeAg) and antibody (anti-HBe). The knowledge base of Hepaxpert-I contains 13 If-Then rules for hepatitis A and 106 If-Then rules for hepatitis B serology. Formally, knowledge acquisition was done by forming a partition of each of the two sets of possible serologic finding patterns that contain patterns of serologic test results, 64 for hepatitis A and 4096 for hepatitis B, respectively. After entering an input pattern of serologic test results in Hepaxpert-I, a rule pattern matching algorithm based on indexing is internally employed as efficient access method for providing the respective interpretive text. Since 1 September 1989, Hepaxpert-I has been routinely applied at the Hepatitis Serology Laboratory of the 2nd Department of Gastroenterology and Hepatology at the University of Vienna Medical School (Vienna General Hospital). Beforehand, a retrospective evaluation of the expert system based on 23,368 hepatitis A and 24,071 hepatitis B serology requests was carried out.
NST-EXPERT is a medical expert system designed to evaluate fetal condition (especially in 'high risk' pregnancies). The system reasons based on data from the Non-Stress Test (NST), a monitoring test widely used in obstetrics. From specific limited maternal-fetal context information together with the analysis of the NST results the system infers a diagnosis for each case, elaborates a therapeutic plan, and suggests a prognosis of an early neonatal outcome. This article describes a new version based on a previous research prototype. Such systems can aid clinicians of varied experience in assessing and managing complicated pregnancies.
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