Predictive modeling of treatment resistant depression using data from STARD and an independent clinical study

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from t...

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Bibliographic Details
Published in:	PloS one Vol. 13; no. 6; p. e0197268
Main Authors:	Nie, Zhi, Vairavan, Srinivasan, Narayan, Vaibhav A, Ye, Jieping, Li, Qingqin S
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 07-06-2018 Public Library of Science (PLoS)
Subjects:	Adult Antidepressants Antidepressive Agents - administration & dosage Anxiety disorders Artificial intelligence Bioinformatics Biomarkers Care and treatment Clinical trials Cluster analysis Clustering Computer and Information Sciences Computer engineering Computer science Databases, Factual Datasets Depressive Disorder, Major - drug therapy Depressive Disorder, Major - physiopathology Dosage and administration Drug Resistance Electrical engineering Female Gene expression Generalized linear models Humans Information technology Learning algorithms Machine Learning Major depressive disorder Male Mathematical models Medicine and Health Sciences Mental depression Model testing Modelling Models, Biological Neurosciences Physical Sciences Prediction models Predictive Value of Tests R&D Remission Research & development Research and Analysis Methods Resistance factors Risk analysis Risk Factors Training Upper bounds Vector quantization Ann Arbor Michigan United States > US
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