@inproceedings{89f893e1920a47d3b37cd10f00c3d2fd,
title = "Standardizing drug adverse event reporting data",
abstract = "Normalizing data in the Adverse Event Reporting System (AERS), an FDA database, would improve the mining capacity of AERS for drug safety signal detection. In this study, we aim to normalize AERS and build a publicly available normalized Adverse drug events (ADE) data source.he drug information in AERS is normalized to RxNorm, a standard terminology source for medication. Drug class information is then obtained from the National Drug File-Reference Terminology (NDF-RT). Adverse drug events (ADE) are aggregated through mapping with the PT (Preferred Term) and SOC (System Organ Class) codes of MedDRA. Our study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). The AERS-DM could provide more perspectives to mine AERS database for drug safety signal detection and could be used by research community in the data mining field.",
keywords = "Adverse drug events, adverse event reporting system (AERS), data mining, data normalization, knowledge discovery",
author = "Liwei Wang and Guoqian Jiang and Dingcheng Li and Hongfang Liu",
year = "2013",
month = jan,
day = "1",
doi = "10.3233/978-1-61499-289-9-1101",
language = "English (US)",
isbn = "9781614992882",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
number = "1-2",
booktitle = "MEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics",
edition = "1-2",
note = "14th World Congress on Medical and Health Informatics, MEDINFO 2013 ; Conference date: 20-08-2013 Through 23-08-2013",
}