Treatment selection using prototyping in latent-space with application to depression treatment
Final Published Version
AffiliationUniversity of Arizona
MetadataShow full item record
PublisherPublic Library of Science
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.
RightsCopyright © 2021 Kleinerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis 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 firstname.lastname@example.org.
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.
NoteOpen access journal
VersionFinal published version
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.