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Erschienen in:
Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Nikola K. Kasabov

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

Verlag: 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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Vorheriges Kapitel A Computational Framework for Personalised Modelling. Applications in Bioinformatics
Nächstes Kapitel Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment
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Metadaten
Titel
Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
verfasst von
Nikola K. Kasabov
Copyright-Jahr
2019
Verlag
Springer Berlin Heidelberg
Buch
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-Jahr: 2019

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