| Literature DB >> 27477839 |
Taxiarchis Botsis1, Christopher Jankosky2, Deepa Arya2, Kory Kreimeyer2, Matthew Foster2, Abhishek Pandey2, Wei Wang3, Guangfan Zhang3, Richard Forshee2, Ravi Goud2, David Menschik2, Mark Walderhaug2, Emily Jane Woo2, John Scott2.
Abstract
We have developed a Decision Support Environment (DSE) for medical experts at the US Food and Drug Administration (FDA). The DSE contains two integrated systems: The Event-based Text-mining of Health Electronic Records (ETHER) and the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment (PANACEA). These systems assist medical experts in reviewing reports submitted to the Vaccine Adverse Event Reporting System (VAERS) and the FDA Adverse Event Reporting System (FAERS). In this manuscript, we describe the DSE architecture and key functionalities, and examine its potential contributions to the signal management process by focusing on four use cases: the identification of missing cases from a case series, the identification of duplicate case reports, retrieving cases for a case series analysis, and community detection for signal identification and characterization. Published by Elsevier Inc.Keywords: Information retrieval; Natural language processing; Network analysis; Post-marketing surveillance; Text mining
Mesh:
Year: 2016 PMID: 27477839 DOI: 10.1016/j.jbi.2016.07.023
Source DB: PubMed Journal: J Biomed Inform ISSN: 1532-0464 Impact factor: 6.317