AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.
Drug safety
Sep 27, 2024
Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central e...
Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach.
Computers in biology and medicine
Sep 26, 2024
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...
Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.
JMIR medical informatics
Sep 24, 2024
BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse ...
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions.
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e., side eff...
Machine Learning Prediction of On/Off Target-driven Clinical Adverse Events.
Pharmaceutical research
Aug 2, 2024
OBJECTIVE: Currently, 90% of clinical drug development fails, where 30% of these failures are due to clinical toxicity. The current extensive animal toxicity studies are not predictive of clinical adverse events (AEs) at clinical doses, while current...
A machine learning technology for addressing medication-related risk in older, multimorbid patients.
The American journal of managed care
Aug 1, 2024
OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitali...
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...
Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN).
BMC medical imaging
Jul 15, 2024
Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interact...
Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records.
International journal of medical informatics
Jul 9, 2024
BACKGROUND: Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge in healthcare, ranging from mild discomfort to severe complications, and can impact patient sa...
Optimizing drug therapy for older adults: shifting away from problematic polypharmacy.
Expert opinion on pharmacotherapy
Jul 2, 2024
INTRODUCTION: The accelerated discovery and production of pharmaceutical products has resulted in many positive outcomes. However, this progress has also contributed to problematic polypharmacy, one of the rapidly growing threats to public health in ...
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