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2019 | OriginalPaper | Chapter

18. Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics

Author : Nikola K. Kasabov

Published in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Publisher: Springer Berlin Heidelberg

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Abstract

The chapter presents methods for building personalised models (PM) for accurate prediction of an outcome for the individual. The general framework for PM from Chap. 17 is here further developed for using brain inspired SNN architectures (BI-SNN). The latter ones facilitate integrated modelling of both static and dynamic (temporal) data related to an individual and groups of individuals. Case studies on predicting stroke and response to treatment are presented in details.

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previous chapter A Computational Framework for Personalised Modelling. Applications in Bioinformatics

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Title: Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
Author: Nikola K. Kasabov
Publisher: Springer Berlin Heidelberg
Book: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-662-57715-8_18

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