Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study.

Background: The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources.

Objective: Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA.

UMLS users and uses: a current overview.

The US National Library of Medicine regularly collects summary data on direct use of Unified Medical Language System (UMLS) resources. The summary data sources include UMLS user registration data, required annual reports submitted by registered users, and statistics on downloads and application programming interface calls. In 2019, the National Library of Medicine analyzed the summary data on 2018 UMLS use.

Toward multimodal signal detection of adverse drug reactions.

Objective: Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources.

Leveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies.

Background: Traditional approaches to pharmacovigilance center on the signal detection from spontaneous reports, e.g., the U.

A time-indexed reference standard of adverse drug reactions.

Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance. The development and evaluation of these methodologies requires proper reference benchmarks.