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Feasibility Study of an Interactive Medical Diagnostic Wikipedia
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SYSNO ASEP 0464681 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Feasibility Study of an Interactive Medical Diagnostic Wikipedia Author(s) Grim, Jiří (UTIA-B) RID, ORCID Number of authors 1 Source Title SPMS 2016 Stochastic and Physical Monitoring Systems. - Prague : Czech Technical University, 2016 - ISBN 978-80-01-06040-7 Pages s. 31-45 Number of pages 15 s. Publication form Medium - C Action SPMS 2016 Stochastic and Physical Monitoring Systems Event date 20.06.2016-24.06.2016 VEvent location Prague - Dobřichovice Country CZ - Czech Republic Event type EUR Language eng - English Country CZ - Czech Republic Keywords Multivariate statistics ; Medical diagnostics ; Product mixtures ; Incomplete data ; Sequential classification ; EM algorithm Subject RIV IN - Informatics, Computer Science R&D Projects GA14-02652S GA ČR - Czech Science Foundation (CSF) GA14-10911S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation Considering 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2017
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