Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis.

ABSTRACT:

Background

Adverse drug events (ADEs) detection and assessment is at the center of pharmacovigilance. Data mining of systems, such as FDA's Adverse Event Reporting System (AERS) and more recently, Electronic Health Records (EHRs), can aid in the automatic detection and analysis of ADEs. Although different data mining approaches have been shown to be valuable, it is still crucial to improve the quality of the generated signals.

Objective

To leverage structural similarity by developing molecular fingerprint-based models (MFBMs) to strengthen ADE signals generated from EHR data.

Methods

A reference standard of drugs known to be causally associated with the adverse event pancreatitis was used to create a MFBM. Electronic Health Records (EHRs) from the New York Presbyteria

SUBMITTER: Vilar S 

PROVIDER: S-EPMC3404072 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature