AIMC Topic: Drug Labeling
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Machine learning-guided Orbitrap-HRAMS-based metabolomic fingerprinting for geographical origin, variety and tissue specific authentication, and adulteration detection of turmeric and ashwagandha.
Food chemistry
Mar 28, 2025
The increasing global demand for herbs and spices in food and nutraceutical industries highlights their key functional benefits, including antioxidant and anti-inflammatory properties. Ensuring authenticity and traceability is essential to counteract...
Evaluating a generative artificial intelligence accuracy in providing medication instructions from smartphone images.
Journal of the American Pharmacists Association : JAPhA
Nov 6, 2024
BACKGROUND: The Food and Drug Administration mandates patient labeling materials like the Medication Guide (MG) and Instructions for Use (IFU) to support appropriate medication use. However, challenges such as low health literacy and difficulties nav...
Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.
Drug safety
Jul 31, 2024
INTRODUCTION: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse ev...
Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.
Expert opinion on drug safety
May 6, 2024
INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medic...
Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents.
Chemical research in toxicology
Jul 24, 2023
The US Food and Drug Administration (FDA) regulatory process often involves several reviewers who focus on sets of information related to their respective areas of review. Accordingly, manufacturers that provide submission packages to regulatory agen...
Adverse drug event detection using reason assignments in FDA drug labels.
Journal of biomedical informatics
Sep 2, 2020
Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs pose significant health and financial problems worldwide. Information about ADEs can inform health care and improve patient safety. However, much of thi...
A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan.
BMC health services research
Apr 15, 2020
BACKGROUND: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to preven...
Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels.
BMC bioinformatics
Dec 23, 2019
BACKGROUND: Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying A...
Natural language processing-based assessment of consistency in summaries of product characteristics of generic antimicrobials.
Pharmacology research & perspectives
Nov 11, 2018
To investigate consistency in summaries of product characteristics (SmPCs) of generic antimicrobials, we used natural language processing (NLP) to analyze and compare large amounts of text quantifying consistency between original and generic SmPCs. W...
Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.
Yearbook of medical informatics
Aug 29, 2018
OBJECTIVES: To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies.
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