AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Enhancing search strategies for systematic reviews on drug Harms: An evaluation of the utility of ChatGPT in error detection and keyword generation.
Computers in biology and medicine
Jul 1, 2025
OBJECTIVE: Developing search strategies for synthesizing evidence on drug harms requires specialized expertise and knowledge. The aim of this study was to evaluate ChatGPT's ability to enhance search strategies for systematic reviews of drug harms by...
A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.
Bioinformatics (Oxford, England)
Jun 2, 2025
MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the populatio...
Developmental toxicity: artificial intelligence-powered assessments.
Trends in pharmacological sciences
Jun 1, 2025
Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about developmental toxicity, that is, drug-induced toxicity and adverse effects in pregnant women and fetus...
PregAN-NET: Addressing Class Imbalance with GANs in Interpretable Computational Framework for Predicting Safety Profile of Drugs Considering Adverse Reactions During Pregnancy.
Journal of biomedical informatics
Jun 1, 2025
Adverse Drug Reactions (ADRs) during pregnancy pose significant risks to both the mother and the fetus. Conventional approaches to predict ADR are inadequate due to ethical restrictions that prevent performing medication studies in pregnant women, le...
Prediction of Drug-Induced Nephrotoxicity Using Chemical Information and Transcriptomics Data.
Journal of chemical information and modeling
May 26, 2025
Prediction of drug-induced nephrotoxicity is an important task in the drug discovery and development pipeline. Chemical information-based machine learning models are used in general for nephrotoxicity prediction as a part of computational modeling. C...
Multi-Knowledge Graph and Multi-View Entity Feature Learning for Predicting Drug-Related Side Effects.
Journal of chemical information and modeling
May 26, 2025
Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side ef...
Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis.
Journal of biomedical semantics
May 23, 2025
BACKGROUND: Vaccines are crucial for preventing infectious diseases; however, they may also be associated with adverse events (AEs). Conventional analysis of vaccine AEs relies on manual review and assignment of AEs to terms in terminology or ontolog...
A robust and statistical analyzed predictive model for drug toxicity using machine learning.
Scientific reports
May 23, 2025
Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast t...
Detecting Adverse Drug Events in Clinical Notes Using Large Language Models.
Studies in health technology and informatics
May 15, 2025
Monitoring adverse drug events (ADEs) is critical for pharmacovigilance and patient safety. However, identifying ADEs remains challenging, as suspected or confirmed side effects are often documented solely in the unstructured text of electronic healt...
Leveraging Large Language Models for Synthetic Data Generation to Enhance Adverse Drug Event Detection in Tweets.
Studies in health technology and informatics
May 15, 2025
Adverse drug event (ADE) detection in social media texts poses significant challenges due to the informal nature of the text and the limited availability of annotations. The scarcity of ADE named entity recognition (NER) datasets for social media hin...
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