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

Justin Mower | Zendy; Devika Subramanian | Zendy; Trevor Cohen | Zendy

Open Access

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

Author(s) -

Justin Mower,

Devika Subramanian,

Trevor Cohen

Publication year - 2018

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocy077

Subject(s) - leverage (statistics) , computer science , artificial intelligence , machine learning , pharmacovigilance , natural language processing , unsupervised learning , set (abstract data type) , generalization , drug , mathematics , psychology , mathematical analysis , psychiatry , programming language

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.

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