Personalised modelling with spiking neural networks integrating temporal and static information.

Doborjeh, M; Kasabov, N; Doborjeh, Z; Enayatollahi, R; Tu, E; Gandomi, AH

Personalised modelling with spiking neural networks integrating temporal and static information.

Doborjeh, M Kasabov, N Doborjeh, Z Enayatollahi, R Tu, E Gandomi, AH

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural networks : the official journal of the International Neural Network Society, 2019, 119, pp. 162-177
Issue Date:: 2019-11

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Field	Value	Language
dc.contributor.author	Doborjeh, M
dc.contributor.author	Kasabov, N
dc.contributor.author	Doborjeh, Z
dc.contributor.author	Enayatollahi, R
dc.contributor.author	Tu, E
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2020-05-08T05:31:47Z
dc.date.available	2019-07-25
dc.date.available	2020-05-08T05:31:47Z
dc.date.issued	2019-11
dc.identifier.citation	Neural networks : the official journal of the International Neural Network Society, 2019, 119, pp. 162-177
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/140589
dc.description.abstract	This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Neural networks : the official journal of the International Neural Network Society
dc.relation.isbasedon	10.1016/j.neunet.2019.07.021
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Learning
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Learning
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Learning
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Pattern Recognition, Automated
dc.title	Personalised modelling with spiking neural networks integrating temporal and static information.
dc.type	Journal Article
utslib.citation.volume	119
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-08T05:31:45Z
pubs.publication-status	Published
pubs.volume	119
utslib.start-page	162

Abstract:

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual.

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http://hdl.handle.net/10453/140589