AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Predicting the frequencies of drug side effects.

Nature communications Sep 11, 2020
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computati...
Pharmaceutical Preparations Drug-Related Side Effects and Adverse Reactions Probability Humans Machine Learning Algorithms Computational Biology Databases, Pharmaceutical
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Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management.

Scientific reports Sep 3, 2020
Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is ...
Semantics Biological Ontologies Database Management Systems Risk Management Humans Pharmaceutical Preparations Drug Development Drug-Related Side Effects and Adverse Reactions
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Adverse drug event detection using reason assignments in FDA drug labels.

Journal of biomedical informatics Sep 2, 2020
Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs pose significant health and financial problems worldwide. Information about ADEs can inform health care and improve patient safety. However, much of thi...
Drug Labeling Humans Natural Language Processing Deep Learning Pharmaceutical Preparations Drug-Related Side Effects and Adverse Reactions
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Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice.

PloS one Jul 29, 2020
Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction...
Kidney Logistic Models Models, Statistical Aged Risk Assessment Aged, 80 and over Adolescent Drug-Related Side Effects and Adverse Reactions Young Adult Vancomycin Adult Humans Prognosis Analysis of Variance Male Neural Networks, Computer Retrospective Studies Middle Aged Female
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BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.

Medical & biological engineering & computing Jul 24, 2020
Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and a...
Models, Statistical Databases, Protein Drug Interactions Humans Clinical Trials as Topic Databases, Chemical Databases, Pharmaceutical Drug-Related Side Effects and Adverse Reactions Knowledge Bases Biological Ontologies Toxicogenetics
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Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.

EBioMedicine Jun 18, 2020
BACKGROUND: Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.
Drug-Related Side Effects and Adverse Reactions Humans Machine Learning PubMed
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Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

Scientific reports Jun 12, 2020
During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alt...
Humans Machine Learning Animals Gene Expression Profiling Models, Theoretical Uncertainty Drug-Related Side Effects and Adverse Reactions Quality Control
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Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Molecular pharmaceutics Jun 8, 2020
Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank da...
Biopharmaceutics Liver Bayes Theorem Drug-Related Side Effects and Adverse Reactions Drug Approval Algorithms Chemical and Drug Induced Liver Injury Databases, Factual Machine Learning Databases, Pharmaceutical Computer Simulation Software Humans
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Borrowing external information to improve Bayesian confidence propagation neural network.

European journal of clinical pharmacology Jun 1, 2020
PURPOSE: A Bayesian confidence propagation neural network (BCPNN) is a signal detection method used by the World Health Organization Uppsala Monitoring Centre to analyze spontaneous reporting system databases. We modify the BCPNN to increase its sens...
Databases, Factual Computer Simulation Humans Drug-Related Side Effects and Adverse Reactions Bayes Theorem Neural Networks, Computer Adverse Drug Reaction Reporting Systems
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Quantitative Prediction of Hemolytic Toxicity for Small Molecules and Their Potential Hemolytic Fragments by Machine Learning and Recursive Fragmentation Methods.

Journal of chemical information and modeling May 15, 2020
Hemolytic toxicity, as one of the key toxicity endpoints for small molecules, can cause lysis of the erythrocyte membrane and subsequent release of hemoglobin into blood plasma, leading to multiple acute and chronic adverse effects. Hence, it is nece...
Drug Discovery Drug-Related Side Effects and Adverse Reactions Humans Machine Learning Software
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