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A feature representation learning method for temporal datasets
Vrije University of Amsterdam, Netherlands.
Vrije University of Amsterdam, Netherlands.
Vrije University of Amsterdam, Netherlands.
Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0003-4753-6745
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2016 (English)In: PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), IEEE , 2016Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE , 2016.
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:liu:diva-138325DOI: 10.1109/SSCI.2016.7849890ISI: 000400488300066ISBN: 978-1-5090-4240-1 (print)OAI: oai:DiVA.org:liu-138325DiVA: diva2:1109033
Conference
IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
Note

Funding Agencies|EU FP7 project E-COMPARED [603098]

Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2017-06-13

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Andersson, GerhardVernmark, Kristofer
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf