AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Toxigraphnet: a graph neural network framework for precise toxicity prediction of drug molecules.

Journal of computer-aided molecular design
Accurate prediction of a drug molecule's toxicity is a critical step in pharmaceutical research, offering the potential to reduce experimental costs, mitigate adverse effects, and accelerate drug development. Traditional computational methods often r...

Signal detection in pharmacovigilance: Methods, tools, and workflows from case identification to adverse drug reaction database entry.

Przeglad epidemiologiczny
Adverse drug reactions (ADRs) remain a major, yet largely preventable, global public health challenge, causing significant morbidity, mortality, and healthcare costs. This review synthesises evidence on the global burden, pharmacovigilance systems, a...

High-Throughput Computing to Detect Harmful Drug-Drug Interactions in Older Adults: Protocol for a Population-Based Cohort Study.

JMIR research protocols
BACKGROUND: Drug-drug interactions (DDIs) are a major concern, especially for older adults taking multiple medications. Although Health Canada and the US Food and Drug Administration (FDA) use population-based studies to identify adverse drug events,...

MVSL-DSF: Multiview Subspace Representation Learning and Cross-Modal Feature Dynamic Aggregation for Enhanced Drug Side Effect Frequency Prediction.

Journal of chemical information and modeling
Drug side effects increase morbidity and mortality in the relevant medical fields. Assessing the frequency of drug side effects is crucial for drug development and risk-effect analysis. Most current research approaches focus on modeling heterogeneous...

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.

Journal of medical Internet research
BACKGROUND: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care ...

Uncertainty-Aware Deep Learning and Structural Feature Analysis for Reliable Nephrotoxicity Prediction.

Journal of chemical information and modeling
Nephrotoxicity remains a critical safety concern in drug development and clinical practice. Despite their significance, existing computational models for nephrotoxicity prediction face challenges related to limited precision and reliability. To addre...

The need for guardrails with large language models in pharmacovigilance and other medical safety critical settings.

Scientific reports
Large language models (LLMs) are useful tools with the capacity for performing specific types of knowledge work at an effective scale. However, LLM deployments in high-risk and safety-critical domains pose unique challenges, notably the issue of "hal...

The evolving role of multimodal imaging, artificial intelligence and radiomics in the radiologic assessment of immune related adverse events.

Clinical imaging
Immunotherapy, in particular checkpoint blockade, has revolutionized the treatment of many advanced cancers. Imaging plays a critical role in assessing both treatment response and the development of immune toxicities. Both conventional imaging and mo...

Development and external validation of a machine learning model for predicting drug-induced immune thrombocytopenia in a real-world hospital cohort.

BMC medical informatics and decision making
BACKGROUND: Drug-induced immune thrombocytopenia (DITP) is a rare but potentially life-threatening adverse drug reaction, often underrecognized due to its nonspecific presentation and the lack of real-time diagnostic tools. Early identification of at...

Predicting clozapine-induced adverse drug reaction biomarkers using machine learning.

Scientific reports
Clozapine is an atypical antipsychotic used for patients with treatment-resistant schizophrenia. This drug has serious adverse drug reactions (ADRs), including the risk of severe neutropenia (agranulocytosis). Patients who could benefit from clozapin...