Structured Product Labeling Improves Detection of Drug-Intolerance Issues

Trouble Spots in Online Direct-to-Consumer Prescription Drug Promotion: A Content Analysis of FDA Warning Letters

Background For the purpose of understanding the Food and Drug Administration’s (FDA’s) concerns regarding online promotion of prescription drugs advertised directly to consumers, this study examines notices of violations (NOVs) and warning letters issued by the FDA to pharmaceutical manufacturers.   Methods The FDA’s warning letters and NOVs, which were issued to pharmaceutical companies over a...

[Drug syrups: Errors in drug labels with possible consequences in patients with heritary fructose intolerance].

Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.

BACKGROUND Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a ...

An Interactive User Interface for Drug Labeling to Improve Readability and Decision-Making

FDA-approved prescribing information (also known as product labeling or labels) contain critical safety information for health care professionals. Drug labels have often been criticized, however, for being overly complex, difficult to read, and rife with overwarning, leading to high cognitive load. In this project, we aimed to improve the usability of drug labels by increasing the 'signal-to-no...

Title: A Supervised Machine Learning Framework for the Extraction of Drug-Drug Interactions from Structured Product Labels Authors and affiliations:

Background: Information about drug-drug interactions (DDIs) is found in the medical literature and in drug package inserts published on DailyMed in addition to commercial drug databases. Objectives: To develop a machine learning framework for the extraction of DDIs from structured product labels (SPLs). Methods: We develop a supervised machine learning framework (support vector machine classifi...