Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 8846713

Multiparameter case studies using knowledge-based systems in hematology.

L W Diamond·P B Tamino·A H Seal·D T Nguyen
View original source ↗Export metadata ↓

Abstract

Knowledge-based systems in diagnostic medicine are often used for teaching medical decision making. We have extended the educational value of our suite of hematology decision support systems (peripheral blood analysis, flow cytometry immunophenotyping and DNA analysis, and bone marrow morphology) by creating case studies in a hypertext format. The case studies use the knowledge-based system screens as a background. Digitized images of blood and bone marrow smears from the teaching cases can be accessed on-line. The case studies, which are designed primarily for technologists and physicians, emphasize the proper multiparameter approach to hematopathology diagnosis.

Explore related subjects

Decision Support TechniquesDiagnosis, Computer-AssistedExpert SystemsHematologic DiseasesHematologyHumansMedical Laboratory PersonnelSystems IntegrationCase ReportsJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L W Diamond, P B Tamino, A H Seal, D T Nguyen. Multiparameter case studies using knowledge-based systems in hematology.. https://doi.org/10.1016/0169-2607(95)01661-c

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed1999

Explicit temporal models for decision-theoretic planning of clinical management.

The management of patients over a prolonged period of time is a complicated task involving both diagnostic and prognostic reasoning with incomplete and often uncertain knowledge. Various formalisations of this type of task exist, but these often conceal one or more essential ingredients of the problem. This article explores the suitability of partially observable Markov decision processes to formalising the planning of clinical management. These processes allow for explicit representation of clinical states of the patient, the management strategy employed, the objectives of treatment, and the role of time and change in reasoning. However, practical application is hampered by their coarse representational granularity and complex formulation. It is discussed how probabilistic network representations can be used to alleviate these obstacles. The resulting method is illustrated with a real-world example from the domain of paediatric cardiology.

Decision Support Techniques↗