AZpharm metaalert: A meta-learning framework for pharmacovigilance

Xiao Liu, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pharmacovigilance is the research related to the detection, assessment, understanding, and prevention of adverse drug events. Despite the research efforts in pharmacovigilance in recent year, current approaches are insufficient in detecting adverse drug reaction (ADR) signals timely across different datasets. In this study, we develop an integrated and high-performance AZ Pharm Meta-Alert framework for efficient and accurate post-approval pharmacovigilance. Our approach extracts adverse drug events from patient social media, electronic health records, and FDA’s Adverse Event Reporting System (FAERS) and integrates ADR signals with stacking and bagging methods. Experiment results show that our approach achieves 71% in precision, 90% in recall, and 80% in f-measure for ADR signal detection and significantly outperforms the traditional signal detection methods.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2016, Revised Selected Papers
PublisherSpringer Verlag
Pages147-154
Number of pages8
Volume10219 LNCS
ISBN (Print)9783319598574
StatePublished - 2017
EventInternational Conference for Smart Health, ICSH 2016 - Haikou, China
Duration: Dec 24 2016Dec 25 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10219 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference for Smart Health, ICSH 2016
CountryChina
CityHaikou
Period12/24/1612/25/16

Keywords

  • Adverse drug event
  • Deep-learning
  • Drug safety surveillance
  • Meta-learning
  • Pharmacovigilance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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