AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Drug-Related Side Effects and Adverse Reactions

Showing 181 to 190 of 305 articles

Clear Filters

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Journal of chemical information and modeling
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically ...

Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.

Artificial intelligence in medicine
Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals h...

Normalizing Spontaneous Reports Into MedDRA: Some Experiments With MagiCoder.

IEEE journal of biomedical and health informatics
Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language...

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges.

Pharmacotherapy
The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing amount of data suggesting that a large amount of patient harm is preventable and can be mitigated wit...

A meta-learning framework using representation learning to predict drug-drug interaction.

Journal of biomedical informatics
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.

BMC bioinformatics
BACKGROUND: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adve...

Next-Generation Machine Learning for Biological Networks.

Cell
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets an...

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

Journal of biomedical semantics
BACKGROUND: Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Pr...

Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.

Journal of medical toxicology : official journal of the American College of Medical Toxicology
Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of such vulnerable patients. Current practice, however, is limited by inaccurate reporting of medication...

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

Journal of biomedical informatics
INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to ...