Expert System Design in Hematology Diagnosis

Nguyen, David; Diamond, Lawrence W.; Priolet, G; C, Sultan

doi:10.1055/s-0038-1634864

Cited by 9 publications

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Computer-assisted interpretation of flow cytometry data in hematology

Thews

Huber

et al. 1996

Cytometry

View full text Add to dashboard Cite

A computer program has been developed for computer‐assisted diagnosis (including subclassification) of flow cytometry data of acute leukaemias and non‐Hodgkin lymphomas by means of artificial intelligence. The knowledge base for the system has been formulatedas semantic networks that describe physiological hematopoiesisas well as the pathological situation (eg., aberrant antigen expression) of hematological disorders. The semantic networks reflect the hierarchy of cells and their occurrence in diseases, the normal and pathological antigen expression patterns of cells, cell maturation, and the frequency of cell populations in normal blood and bone marrow. Using these semantic networks, the diagnosis algorithm compares the characteristic antigen expression pattern of a disease with the actual findings in the blood or bone marrow sample. The algorithm can separate mixed populations by taking double staining findings into account. Finally, a diagnosis text is generated that describes all identified cell populations and the resulting diagnosis. The validationof the program showed a correct diagnosis (disease group and subclassification) in 97% of the cases (n = 633) with slight differences between the disease groups (e.g., B‐NHL: 99%, B‐cell ALL: 84%). © 1996 Wiley‐Liss, Inc.

show abstract

Computer-assisted interpretation of flow cytometry data in hematology

Thews

Huber

et al. 1996

Cytometry

View full text Add to dashboard Cite

show abstract

Field evaluations of a knowledge-based system for peripheral blood interpretation

Diamond

Nguyen

Ralph³

et al. 1995

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

2011

View full text Add to dashboard Cite

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

show abstract

Expert System Design in Hematology Diagnosis

Cited by 9 publications

References 13 publications

Computer-assisted interpretation of flow cytometry data in hematology

Computer-assisted interpretation of flow cytometry data in hematology

Field evaluations of a knowledge-based system for peripheral blood interpretation

Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Contact Info

Product

Resources

About