A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Oct 1, 2018

Abstract

OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs.

Authors

Behrooz Davazdahemami

Department of Management Science and Information Systems, Oklahoma State University, Stillwater, Oklahoma, USA.
Dursun Delen

Department of Management Science and Information Systems, Center for Health Systems Innovation, Oklahoma State University, Stillwater, OK, USA.

Keywords

Adverse Drug Reaction Reporting Systems Algorithms Computer Communication Networks Datasets as Topic Drug Interactions Drug-Related Side Effects and Adverse Reactions Humans Machine Learning Models, Theoretical Pharmacovigilance

External Resources

View on PubMed Access via DOI PubMed (30085102)

A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Abstract

Authors

Keywords

External Resources

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