A feature representation learning method for temporal datasets

Ward van Breda, Mark Hoogendoorn, Guszti Eiben, Gerhard Andersson, Heleen Riper, Jeroen Ruwaard, Kristofer Vernmark

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages1-8
Number of pages8
ISBN (Electronic)978-1-5090-4240-1
DOIs
Publication statusPublished - 9 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

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