Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-59858-1_14 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Pharmacovigilance,Adverse drug event,Meta-learning,Deep-learning,Drug safety surveillance | Conference | 10219 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ying liu | 1 | 364 | 46.92 |
| Hsinchun Chen | 2 | 9569 | 813.33 |