Genetic markers on chromosome 19p and prenatal diagnosis of HLA class II-deficient combined immunodeficiency.

HLA class II-deficient combined immunodeficiency (CID) is an inherited disease characterized by a total lack of HLA class II gene expression, due to a regulatory defect affecting these genes. In the family investigated the disease phenotype occurs parallel to an abnormal structural feature of the CD23 antigen. We sequenced parts of the FCER2 gene coding for CD23 and found a restriction fragment length polymorphism (RFLP) that cosegregates with the disease. Analysis of recombinant haplotypes by microsatellites mapping to the chromosomal region 19p13.3 suggests that the disease locus maps between FCER2 and the microsatellite marker D19S424, probably close to D19S216 and D19S177. These data may offer the possibility of a rapid and early prenatal diagnosis of a subgroup of patients with HLA class II-deficient CID.

Application of a network structure in a knowledge-based system for medical diagnostics.

Systems supporting diagnostic decision making have been designed more often on the base of production rules than on semantic nets. One reason for that might be that concepts to process information on the latter structures are still rare. In this study the application of a semantic net is described. Via creation of various views, different sets of information are defined, depending on their meaning in the semantic net. Projections, which are in general a combination of mappings and set operations, are used to infer a set of information from another. The developed system was written in Pascal, the semantic net was implemented using a network-like pointer structure. The concept has been used to develop PROJECTOR, an expert system for differential diagnosis based on a semantic net. The structure has turned out to be fast and compact enough to allow large amounts of data to be handled easily even with a personal computer.

Development of a computer-aided reference system for differential diagnostics support.

In a situation of uncertain diagnosis, physicians may spend valuable time consulting relevant literature, often with unsatisfactory results. Therefore, our aim was to develop a computer system which supports differential diagnostics via rapid and comprehensive searches through information in literature. Based on entered signs and symptoms our prototype is able to offer probable diagnoses. Subsequently, further examinations and tests are suggested to confirm or exclude a certain disease. Thus, the final diagnosis is made gradually by differentiation of possible diseases. Our first attempt consisted in representing knowledge in a rule-based PROLOG system. However, because nearly all information for a sign-oriented differential diagnosis can be represented with very few relations only, we turned to a fact-oriented representation of signs and diseases. Access was possible via PROLOG or an imperative programming language. The index-sequential access on a fact-oriented representation of knowledge was suitable to manage a large knowledge base, which is necessary for a thorough differential diagnosis. A pointer structure was recently examined in order to handle different object-to-object relations. Efficient information processing is now possible which provides short response times using even broad knowledge bases.