AIMC Topic: Drug Interactions

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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 ...
Natural Language Processing Antitubercular Agents Drug Interactions Case-Control Studies Logistic Models Chemical and Drug Induced Liver Injury Drug-Related Side Effects and Adverse Reactions Isoniazid Electronic Health Records Female Male Middle Aged Adult Machine Learning Retrospective Studies Humans
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A Knowledge Graph-Based Method for Drug-Drug Interaction Prediction With Contrastive Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug deve...
Drug Interactions Algorithms Computational Biology Humans Machine Learning
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KGRLFF: Detecting Drug-Drug Interactions Based on Knowledge Graph Representation Learning and Feature Fusion.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Accurate prediction of drug-drug interactions (DDIs) plays an important role in improving the efficiency of drug development and ensuring the safety of combination therapy. Most existing models rely on a single source of information to predict DDIs, ...
Drug Interactions Humans Algorithms Computational Biology Machine Learning
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Geometric Molecular Graph Representation Learning Model for Drug-Drug Interactions Prediction.

IEEE journal of biomedical and health informatics Dec 5, 2024
Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Therefore, it is important to predict potential drug interactions since it can provide combination strategies of drugs...
Humans Deep Learning Computational Biology Drug Interactions Machine Learning Algorithms
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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 Neural Networks, Computer Drug Interactions Deep Learning Drug-Related Side Effects and Adverse Reactions Natural Language Processing
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Predicting the physiological effects of multiple drugs using electronic health record.

Computers in biology and medicine Dec 1, 2024
Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consi...
Middle Aged Drug Interactions Adult Male Humans Female Machine Learning Electronic Health Records Aged
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DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Artificial intelligence in medicine Nov 17, 2024
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions predicti...
Algorithms Humans Computational Biology Pharmaceutical Preparations Drug Interactions Drug Discovery Proteins 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...
Humans Quantitative Structure-Activity Relationship Drug Interactions Pregnancy Outcome Pregnancy Machine Learning Environmental Exposure Drug-Related Side Effects and Adverse Reactions Female Reproduction
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Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.

Methods (San Diego, Calif.) Nov 1, 2024
Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) ...
Diabetes Mellitus Drug Interactions Administration, Oral Machine Learning Humans Hypoglycemic Agents
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Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug-Drug Interaction Prediction.

Journal of chemical information and modeling Oct 30, 2024
The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various...
Algorithms Humans Pharmaceutical Preparations Neural Networks, Computer Drug Interactions
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