@inproceedings{f5f5982a83b84a29b096aaae4bfdf108,
title = "AZpharm metaalert: A meta-learning framework for pharmacovigilance",
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{\textquoteright}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.",
keywords = "Adverse drug event, Deep-learning, Drug safety surveillance, Meta-learning, Pharmacovigilance",
author = "Xiao Liu and Hsinchun Chen",
note = "Funding Information: This work was supported in part by National Science Foundation SBIR/STTR Award ID #1417181 and #1622788.; International Conference for Smart Health, ICSH 2016 ; Conference date: 24-12-2016 Through 25-12-2016",
year = "2017",
doi = "10.1007/978-3-319-59858-1_14",
language = "English (US)",
isbn = "9783319598574",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "147--154",
editor = "Chunxiao Xing and Yong Zhang and Ye Liang",
booktitle = "Smart Health - International Conference, ICSH 2016, Revised Selected Papers",
}