Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Oct 1, 2018

Abstract

OBJECTIVE: The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.

Authors

Justin Mower

Baylor College of Medicine, Houston, Texas;; University of Texas Health Science Center at Houston, Houston, Texas.
Devika Subramanian

Rice University, Houston, Texas.
Trevor Cohen

University of Washington, Seattle, WA.

Keywords

Data Mining Databases, Bibliographic Drug-Related Side Effects and Adverse Reactions Humans Natural Language Processing Pharmacovigilance Product Surveillance, Postmarketing Semantics Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (30010902)

Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Abstract

Authors

Keywords

External Resources

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