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Feasibility Study of an Interactive Medical Diagnostic Wikipedia

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    SYSNO ASEP0464681
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleFeasibility Study of an Interactive Medical Diagnostic Wikipedia
    Author(s) Grim, Jiří (UTIA-B) RID
    Number of authors1
    Source TitleSPMS 2016 Stochastic and Physical Monitoring Systems. - Prague : Czech Technical University, 2016 - ISBN 978-80-01-06040-7
    Pagess. 31-45
    Number of pages15 s.
    Publication formMedium - C
    ActionSPMS 2016 Stochastic and Physical Monitoring Systems
    Event date20.06.2016-24.06.2016
    VEvent locationPrague - Dobřichovice
    CountryCZ - Czech Republic
    Event typeEUR
    Languageeng - English
    CountryCZ - Czech Republic
    KeywordsMultivariate statistics ; Medical diagnostics ; Product mixtures ; Incomplete data ; Sequential classification ; EM algorithm
    Subject RIVIN - Informatics, Computer Science
    R&D ProjectsGA14-02652S GA ČR - Czech Science Foundation (CSF)
    GA14-10911S GA ČR - Czech Science Foundation (CSF)
    Institutional supportUTIA-B - RVO:67985556
    AnnotationConsidering different application possibilities of product distribution mixtures we have proposed three formal tools in the last years, which can be used to accumulate decision-making know-how from particular diagnostic cases. First, we have developed a structural mixture model to estimate multidimensional probability distributions from incomplete and possibly weighted data vectors. Second, we have shown that the estimated product mixture can be used as a knowledge base for the Probabilistic Expert System (PES) to infer conclusions from definite or even uncertain input information. Finally we have shown that, by using product mixtures, we can exactly optimize sequential decision-making by means of the Shannon formula of conditional informativity. We combine the above statistical tools in the framework of an interactive open-access medical diagnostic system with automatic accumulation of decision-making knowledge.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Year of Publishing2017
Number of the records: 1