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dc.contributor.authorBoeker, Matthias
dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorRiegler, Michael
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorJakobsen, Petter
dc.date.accessioned2023-02-27T12:28:47Z
dc.date.available2023-02-27T12:28:47Z
dc.date.issued2022-09-27
dc.description.abstractIn this paper, we address the problem of predicting schizophrenia based on a persons measured motor activity over time. A key challenge to achieve this is how to extract features from the activity data that can efficiently separate schizophrenia patients from healthy subjects. To achieve this, we suggest to fit time dependent hidden Markov models with and without integrated covariates and letting the estimated model parameters represent our features. To further evaluate the efficiency of these features, we suggest to use them as features in a classification method (logistic regression) to separate schizophrenia patients from healthy subjects. The results show that the estimated hidden Markov model parameters are well-performing in predicting schizophrenia, and outperform features derived from other methods in the literature in terms of goodness-of-fit and classification performance.en_US
dc.identifier.citationBoeker, Hammer, Riegler, Halvorsen, Jakobsen. Prediction of schizophrenia from activity data using hidden Markov model parameters. Neural Computing & Applications. 2022en_US
dc.identifier.cristinIDFRIDAID 2075423
dc.identifier.doi10.1007/s00521-022-07845-7
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttps://hdl.handle.net/10037/28614
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.journalNeural Computing & Applications
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titlePrediction of schizophrenia from activity data using hidden Markov model parametersen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)