Treatment selection using prototyping in latent-space with application to depression treatment

Machine-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,...

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Autores principales:	Akiva Kleinerman, Ariel Rosenfeld, David Benrimoh, Robert Fratila, Caitrin Armstrong, Joseph Mehltretter, Eliyahu Shneider, Amit Yaniv-Rosenfeld, Jordan Karp, Charles F. Reynolds, Gustavo Turecki, Adam Kapelner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/340d8807035c463b98f66ee3e0a54178
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spelling	oai:doaj.org-article:340d8807035c463b98f66ee3e0a541782021-11-25T06:11:02ZTreatment selection using prototyping in latent-space with application to depression treatment1932-6203https://doaj.org/article/340d8807035c463b98f66ee3e0a541782021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589171/?tool=EBIhttps://doaj.org/toc/1932-6203Machine-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.Akiva KleinermanAriel RosenfeldDavid BenrimohRobert FratilaCaitrin ArmstrongJoseph MehltretterEliyahu ShneiderAmit Yaniv-RosenfeldJordan KarpCharles F. ReynoldsGustavo TureckiAdam KapelnerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Akiva Kleinerman Ariel Rosenfeld David Benrimoh Robert Fratila Caitrin Armstrong Joseph Mehltretter Eliyahu Shneider Amit Yaniv-Rosenfeld Jordan Karp Charles F. Reynolds Gustavo Turecki Adam Kapelner Treatment selection using prototyping in latent-space with application to depression treatment
description	Machine-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.
format	article
author	Akiva Kleinerman Ariel Rosenfeld David Benrimoh Robert Fratila Caitrin Armstrong Joseph Mehltretter Eliyahu Shneider Amit Yaniv-Rosenfeld Jordan Karp Charles F. Reynolds Gustavo Turecki Adam Kapelner
author_facet	Akiva Kleinerman Ariel Rosenfeld David Benrimoh Robert Fratila Caitrin Armstrong Joseph Mehltretter Eliyahu Shneider Amit Yaniv-Rosenfeld Jordan Karp Charles F. Reynolds Gustavo Turecki Adam Kapelner
author_sort	Akiva Kleinerman
title	Treatment selection using prototyping in latent-space with application to depression treatment
title_short	Treatment selection using prototyping in latent-space with application to depression treatment
title_full	Treatment selection using prototyping in latent-space with application to depression treatment
title_fullStr	Treatment selection using prototyping in latent-space with application to depression treatment
title_full_unstemmed	Treatment selection using prototyping in latent-space with application to depression treatment
title_sort	treatment selection using prototyping in latent-space with application to depression treatment
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/340d8807035c463b98f66ee3e0a54178
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Treatment selection using prototyping in latent-space with application to depression treatment

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