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dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorRuiz, Cristina Soguero
dc.contributor.authorJenssen, Robert
dc.date.accessioned2023-09-27T13:37:19Z
dc.date.available2023-09-27T13:37:19Z
dc.date.issued2020
dc.description.abstractA large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient’s health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCK<sub>IM</sub>, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCK<sub>IM</sub> is particularly well suited if there is a lack of labels-a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.en_US
dc.identifier.citationMikalsen KØ, Ruiz CS, Jenssen R: A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs. In: Shaban-Nejad A, Michalowski M, Buckeridge. Explainable AI in Healthcare and Medicine, 2020. Springeren_US
dc.identifier.cristinIDFRIDAID 1860204
dc.identifier.isbn9783030533519
dc.identifier.issn1860-949X
dc.identifier.issn1860-9503
dc.identifier.urihttps://hdl.handle.net/10037/31250
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.titleA Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRsen_US
dc.type.versionacceptedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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