AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Interpretable prediction of drug-drug interactions via text embedding in biomedical literature.

Computers in biology and medicine Dec 2, 2024
Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicti...
Humans Natural Language Processing Drug Interactions Neural Networks, Computer Deep Learning Drug-Related Side Effects and Adverse Reactions
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Bidirectional Long Short-Term Memory-Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches.

JMIR medical informatics Nov 20, 2024
BACKGROUND: Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to de...
Republic of Korea Social Networking Deep Learning Natural Language Processing Humans Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer
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IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Reproduction (Cambridge, England) Nov 15, 2024
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...
Pregnancy Outcome Environmental Exposure Pregnancy Female Reproduction Quantitative Structure-Activity Relationship Drug Interactions Drug-Related Side Effects and Adverse Reactions Humans Machine Learning
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MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction.

Journal of biomedical informatics Nov 12, 2024
OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, ...
Social Media Natural Language Processing Benchmarking Drug-Related Side Effects and Adverse Reactions Humans Databases, Factual Data Mining Electronic Health Records Adverse Drug Reaction Reporting Systems Machine Learning Algorithms
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Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.

JMIR formative research Nov 8, 2024
BACKGROUND: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene-related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of mo...
Calcitonin Gene-Related Peptide Migraine Disorders Social Media Humans Natural Language Processing Antibodies, Monoclonal Antibodies, Monoclonal, Humanized Drug-Related Side Effects and Adverse Reactions
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HSTrans: Homogeneous substructures transformer for predicting frequencies of drug-side effects.

Neural networks : the official journal of the International Neural Network Society Oct 23, 2024
Identifying the frequencies of drug-side effects is crucial for assessing drug risk-benefit. However, accurately determining these frequencies remains challenging due to the limitations of time and scale in clinical randomized controlled trials. As a...
Humans Neural Networks, Computer Algorithms Drug-Related Side Effects and Adverse Reactions
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Graph neural network-based subgraph analysis for predicting adverse drug events.

Computers in biology and medicine Oct 23, 2024
PURPOSE: Adverse drug events (ADEs) are a significant global public health concern, and they have resulted in high rates of hospital admissions, morbidity, and mortality. Prior to the use of machine learning and deep learning methods, ADEs may not be...
Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions Humans
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Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Journal of chemical information and modeling Oct 21, 2024
Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we pr...
Deep Learning Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Machine Learning Humans
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Predictive, integrative, and regulatory aspects of AI-driven computational toxicology - Highlights of the German Pharm-Tox Summit (GPTS) 2024.

Toxicology Oct 18, 2024
The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13-15, 2024. The event brought together over 700 participants fr...
Machine Learning Animals Humans Artificial Intelligence Drug-Related Side Effects and Adverse Reactions Risk Assessment Germany Computational Biology Toxicology
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Identifying the most critical side effects of antidepressant drugs: a new model proposal with quantum spherical fuzzy M-SWARA and DEMATEL techniques.

BMC medical informatics and decision making Sep 28, 2024
Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. Therefore, priority should be given to the more important ones, among these side effects. However, the nu...
Drug-Related Side Effects and Adverse Reactions Antidepressive Agents Humans Fuzzy Logic
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