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

Journal of the American Medical Informatics Association : JAMIA
Justin MowerTrevor Cohen

Abstract

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. Using ≈80 million concept-relationship-concept triples extracted from the literature using the SemRep Natural Language Processing system, distributed vector representations (embeddings) were generated for concepts as functions of their relationships utilizing two unsupervised representational approaches. Embeddings for drugs and side effects of interest from two widely used reference standards were then composed to generate embeddings of drug/side-effect pairs, which were used as input for supervised machine learning. This methodology was developed and evaluated using cross-validation strategies and compared to contemporary approaches. To qualitatively assess generalization, models trained on the Observational Medical Outcomes Partnership (OMOP) drug/side-effect reference set were evaluated against a list of ≈1100 drugs from an online database. The employed method improved performance over previous approaches. Cross-validation results advance the state of the art (AUC 0.96; F1 0.90 and AUC 0.9...Continue Reading

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Citations

Oct 28, 2019·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·Justin MowerDevika Subramanian
Aug 7, 2019·Trends in Pharmacological Sciences·Anna O BasileNicholas P Tatonetti
Mar 13, 2019·Journal of Biomedical Informatics·Vishrawas GopalakrishnanAidong Zhang

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