Discovering Novel Adverse Drug Events Using Natural Language Processing and Mining of the Electronic Health Record

Authors:
Carol Friedman
Affiliations:
Department of Biomedical Informatics, Columbia University, New York, US
Venue:
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Year:
2009

Citing 1
Cited 4

A statistical methodology for analyzing co-occurrence data from a large sample

Journal of Biomedical Informatics

Mining electronic health records for adverse drug effects using regression based methods

Proceedings of the 1st ACM International Health Informatics Symposium
Identifying potential adverse effects using the web: A new approach to medical hypothesis generation

Journal of Biomedical Informatics
Towards large-scale twitter mining for drug-related adverse events

Proceedings of the 2012 international workshop on Smart health and wellbeing
Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS)

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.01

Visualization

Abstract

This talk presents an overview of our research in use of medical knowledge, natural language processing, the electronic health record, and statistical methods to automatically discover novel adverse drug events, which are serious problems world-wide.