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2023 | OriginalPaper | Buchkapitel

Multi-agent Neural-Like Models for the Integration of Multimodal Medical Examination Data

verfasst von : Zalimkhan Nagoev, Olga Nagoeva, Inna Pshenokova, Kantemir Bzhikhatlov, Irina Gurtueva, Sultan Kankulov

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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Abstract

In the present paper, we explore the possibility of creating a uniform approach to form a factual representation for health information on the basis of multimodal medical examination data. We propose to use a formal approach based on multi-agent neurocognitive architectures to integrate data of this kind in a unified graph of facts and actions. The situationally determined self-organization of program agents-neurons, organized at the nodes of the cognitive architecture, makes it possible to connect afferent data flows of different modalities with the functional representation of semantic ontologies. There are some results of an ongoing multistage computational experiment in the paper.

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Vorheriges Kapitel A Symbolic Regression Approach to Hepatocellular Carcinoma Diagnosis Using Hypermethylated CpG Islands in Circulating Cell-Free DNA

Nächstes Kapitel The Importance of Prognostic Variables to Monitoring Heart Failure Using Health Management Systems

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Titel: Multi-agent Neural-Like Models for the Integration of Multimodal Medical Examination Data
verfasst von: Zalimkhan Nagoev
Olga Nagoeva
Inna Pshenokova
Kantemir Bzhikhatlov
Irina Gurtueva
Sultan Kankulov
Verlag: Springer International Publishing
Buch: Biomedical and Computational Biology
Print ISBN: 978-3-031-25190-0

Electronic ISBN: 978-3-031-25191-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-25191-7_24

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