AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach.

JMIR medical informatics
BACKGROUND: Adverse drug reactions (ADRs) pose serious risks to patient health, and effectively predicting and managing them is an important public health challenge. Given the complexity and specificity of biomedical text data, the traditional contex...

MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.

Scientific reports
Accurate prediction of drug side effect frequencies is critical for drug safety assessment but remains challenging due to the high cost of clinical trials and the limited generalizability of existing models. We propose Multi Fingerprint and Graph Emb...

Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital.

European journal of hospital pharmacy : science and practice
OBJECTIVES: Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, thei...

Pharmacovigilance in the digital age: gaining insight from social media data.

Experimental biology and medicine (Maywood, N.J.)
Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous reporting systems and clinical trials are valuable for identifying adverse drug events, but face de...

Artificial Intelligence: Applications in Pharmacovigilance Signal Management.

Pharmaceutical medicine
Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing monitoring and understanding of those products' safety profiles. Part of this process, signal manageme...

Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International Society of Pharmacovigilance.

Drug safety
Signal management, defined as the set of activities from signal detection to recommendations for action, is conducted using different data sources and leveraging data from spontaneous reporting databases (SRDs), which represent the cornerstone of pha...

Clinical prediction models for medication adverse events in patients with rheumatic and musculoskeletal conditions: A systematic literature review.

Seminars in arthritis and rheumatism
OBJECTIVES: This systematic review aims to identify, summarize, and evaluate the methodological quality of existing clinical prediction models (CPMs) that predict adverse events (AEs) associated with medications prescribed for rheumatic and musculosk...

Medication Safety in Acute Care Settings.

The Medical clinics of North America
This article addresses medication-related problems (MRPs) in hospitals, which continue to cause substantial preventable harm. Medication errors (ME), adverse drug reactions, and adverse drug events remain prevalent, significantly impacting patient sa...

TransformDDI: The Transformer-Based Joint Multi-Task Model for End-to-End Drug-Drug Interaction Extraction.

IEEE journal of biomedical and health informatics
Drug-Drug Interactions (DDI) identification is a part of the drug safety process, that focuses at avoiding potential adverse drug effects that can lead to patient health risks. With the exponential growth in published literature, it becomes increasin...