AI Medical Compendium Topic

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

Drug-Related Side Effects and Adverse Reactions

Showing 21 to 30 of 305 articles

Clear Filters

Identifying the most critical side effects of antidepressant drugs: a new model proposal with quantum spherical fuzzy M-SWARA and DEMATEL techniques.

BMC medical informatics and decision making
Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. Therefore, priority should be given to the more important ones, among these side effects. However, the nu...

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach.

Computers in biology and medicine
Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presen...

HSTrans: Homogeneous substructures transformer for predicting frequencies of drug-side effects.

Neural networks : the official journal of the International Neural Network Society
Identifying the frequencies of drug-side effects is crucial for assessing drug risk-benefit. However, accurately determining these frequencies remains challenging due to the limitations of time and scale in clinical randomized controlled trials. As a...

Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.

JMIR formative research
BACKGROUND: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene-related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of mo...

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Journal of chemical information and modeling
Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we pr...

IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Reproduction (Cambridge, England)
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...

Graph neural network-based subgraph analysis for predicting adverse drug events.

Computers in biology and medicine
PURPOSE: Adverse drug events (ADEs) are a significant global public health concern, and they have resulted in high rates of hospital admissions, morbidity, and mortality. Prior to the use of machine learning and deep learning methods, ADEs may not be...

Predictive, integrative, and regulatory aspects of AI-driven computational toxicology - Highlights of the German Pharm-Tox Summit (GPTS) 2024.

Toxicology
The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13-15, 2024. The event brought together over 700 participants fr...

Machine learning for adverse event prediction in outpatient parenteral antimicrobial therapy: a scoping review.

The Journal of antimicrobial chemotherapy
OBJECTIVE: This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers ...

ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.

IEEE journal of biomedical and health informatics
The medical community has grappled with the challenge of analysis and early detection of severe and unknown adverse drug reactions (ADRs) from Spontaneous Reporting Systems (SRSs) like the FDA Adverse Event Reporting System (FAERS), which often lack ...