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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.
Linköpings universitet, Institutionen för beteendevetenskap och lärande, Psykologi. Linköpings universitet, Filosofiska fakulteten.ORCID-id: 0000-0003-4753-6745
Vise andre og tillknytning
2016 (engelsk)Inngår i: PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), IEEE , 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE , 2016.
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-138325DOI: 10.1109/SSCI.2016.7849890ISI: 000400488300066ISBN: 978-1-5090-4240-1 (tryckt)OAI: oai:DiVA.org:liu-138325DiVA, id: diva2:1109033
Konferanse
IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
Merknad

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

Tilgjengelig fra: 2017-06-13 Laget: 2017-06-13 Sist oppdatert: 2018-12-12

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  • apa
  • harvard1
  • ieee
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Språk
  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
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  • html
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