Open AccessTreatment selection using prototyping in latent-space with application to depression treatment
Author(s) -
Akiva Kleinerman,
Ariel Rosenfeld,
David Benrimoh,
Robert Fratila,
Caitrin Armstrong,
Joseph Mehltretter,
Eliyahu Shneider,
Amit Yaniv-Rosenfeld,
Jordan F. Karp,
Charles F. Reynolds,
Gustavo Turecki,
Adam Kapelner
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0258400
Subject(s) - major depressive disorder , cluster analysis , computer science , depression (economics) , machine learning , personalized medicine , selection (genetic algorithm) , artificial intelligence , psychology , medicine , data science , bioinformatics , clinical psychology , biology , economics , macroeconomics , mood
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.