A chronological pharmacovigilance network analytics approach for predicting adverse drug events | Zendy

Behrooz Davazdahemami | Zendy; Dursun Delen | Zendy

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A chronological pharmacovigilance network analytics approach for predicting adverse drug events

Author(s) -

Behrooz Davazdahemami,

Dursun Delen

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/ocy097

Subject(s) - pharmacovigilance , computer science , drug , machine learning , data mining , analytics , artificial intelligence , set (abstract data type) , construct (python library) , support vector machine , medicine , pharmacology , programming language

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.

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