Faculty, Staff and Student Publications
Publication Date
1-1-2022
Journal
AMIA Summits on Translational Science Proceedings
Abstract
Although pharmaceutical products undergo clinical trials to profile efficacy and safety, some adverse drug reactions (ADRs) are only discovered after release to market. Post-market drug safety surveillance - pharmacovigilance - leverages information from various sources to proactively identify such ADRs. Clinical notes are one source of observational data that could assist this process, but their inherent complexity can obfuscate possible ADR signals. In previous research, embeddings trained on observational reports have improved detection of such signals over commonly used statistical measures. Moreover, neural embedding methods which further encode juxtapositional information have shown promise on analogical retrieval tasks, suggesting proximity-based alternatives to document-level modeling for signal detection. This work uses natural language processing and locality sensitive neural embeddings to increase ADR signal recovery from clinical notes, with AUCs of ~0.63-0.71. Constituting a ~50% increase over baselines, our method sets the state-of-the-art for these reference standards when solely leveraging clinical notes.
PMID
35854716
PMCID
PMC9285153
PubMedCentral® Posted Date
5-23-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Pharmacy and Pharmaceutical Sciences Commons, Translational Medical Research Commons
