A feature representation learning method for temporal datasets

Andersson, Gerhard (author): Linköpings universitet,Psykologi,Filosofiska fakulteten

Vernmark, Kristofer (author): Linköpings universitet,Psykologi,Filosofiska fakulteten,Psykologpartners Linkoping, St T Larsgatan 30, S-58224 Linkoping, Sweden

(creator_code:org_t)

IEEE, 2016
2016
English.
In: PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI). - : IEEE. - 9781509042401

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.1...; show less...

Abstract Subject headings

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.

By the author/editor: van Breda, Ward; Hoogendoorn, Mar ...; Eiben, A. E.; Andersson, Gerha ...; Riper, Heleen; Ruwaard, Jeroen; show more...; Vernmark, Kristo ...; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

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