AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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ADR-DQPU: A Novel ADR Signal Detection Using Deep Reinforcement and Positive-Unlabeled Learning.
IEEE journal of biomedical and health informatics
Feb 10, 2025
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 ...
Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction.
Drug safety
Feb 7, 2025
BACKGROUND: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic ...
Machine Learning-Enabled Drug-Induced Toxicity Prediction.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 3, 2025
Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intellige...
InterDIA: Interpretable prediction of drug-induced autoimmunity through ensemble machine learning approaches.
Toxicology
Jan 25, 2025
Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these ...
Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.
BMC bioinformatics
Jan 15, 2025
In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably...
Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.
Drug safety
Jan 9, 2025
BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence ...
ComNet: A Multiview Deep Learning Model for Predicting Drug Combination Side Effects.
Journal of chemical information and modeling
Jan 3, 2025
As combination therapy becomes more common in clinical applications, predicting adverse effects of combination medications is a challenging task. However, there are three limitations of the existing prediction models. First, they rely on a single vie...
Drug toxicity prediction model based on enhanced graph neural network.
Computers in biology and medicine
Dec 24, 2024
Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms struggle to capture the full scope of molecular structure features, limiting their effectiveness in toxici...
Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment.
JCO clinical cancer informatics
Dec 23, 2024
PURPOSE: Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-re...
Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.
BMC medical research methodology
Dec 20, 2024
BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing ...
Middle Aged
Case-Control Studies
Logistic Models
Natural Language Processing
Antitubercular Agents
Female
Adult
Male
Retrospective Studies
Electronic Health Records
Isoniazid
Drug Interactions
Chemical and Drug Induced Liver Injury
Drug-Related Side Effects and Adverse Reactions
Humans
Machine Learning
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