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dc.contributor.authorKleinerman, A.
dc.contributor.authorRosenfeld, A.
dc.contributor.authorBenrimoh, D.
dc.contributor.authorFratila, R.
dc.contributor.authorArmstrong, C.
dc.contributor.authorMehltretter, J.
dc.contributor.authorShneider, E.
dc.contributor.authorYaniv-Rosenfeld, A.
dc.contributor.authorKarp, J.
dc.contributor.authorReynolds, C.F.
dc.contributor.authorTurecki, G.
dc.contributor.authorKapelner, A.
dc.date.accessioned2021-12-13T23:26:04Z
dc.date.available2021-12-13T23:26:04Z
dc.date.issued2021
dc.identifier.citationKleinerman, A., Rosenfeld, A., Benrimoh, D., Fratila, R., Armstrong, C., Mehltretter, J., Shneider, E., Yaniv-Rosenfeld, A., Karp, J., Reynolds, C. F., Turecki, G., & Kapelner, A. (2021). Treatment selection using prototyping in latent-space with application to depression treatment. PLoS ONE.
dc.identifier.issn1932-6203
dc.identifier.doi10.1371/journal.pone.0258400
dc.identifier.urihttp://hdl.handle.net/10150/662543
dc.description.abstractMachine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today. Copyright: © 2021 Kleinerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.language.isoen
dc.publisherPublic Library of Science
dc.rightsCopyright © 2021 Kleinerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleTreatment selection using prototyping in latent-space with application to depression treatment
dc.typeArticle
dc.typetext
dc.contributor.departmentUniversity of Arizona
dc.identifier.journalPLoS ONE
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitlePLoS ONE
refterms.dateFOA2021-12-13T23:26:04Z


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Copyright © 2021 Kleinerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as Copyright © 2021 Kleinerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.