AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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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...
Machine Learning Animals Gene Expression Profiling Humans Uncertainty Models, Theoretical 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...
Databases, Factual Bayes Theorem Chemical and Drug Induced Liver Injury Machine Learning Humans Drug-Related Side Effects and Adverse Reactions Databases, Pharmaceutical Computer Simulation Software Biopharmaceutics Drug Approval Liver Algorithms
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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 Bayes Theorem Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Humans Neural Networks, Computer
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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...
Humans Machine Learning Software Drug Discovery Drug-Related Side Effects and Adverse Reactions
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MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.

Molecular pharmaceutics Apr 13, 2020
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is ...
Cell Line, Tumor Benchmarking Drug-Related Side Effects and Adverse Reactions Humans Machine Learning Retrospective Studies Drug Discovery
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Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.

Journal of pharmacokinetics and pharmacodynamics Apr 11, 2020
The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and S...
Maternal-Fetal Exchange Preconception Care Pregnancy Complications Decision Making, Shared Perinatal Care Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Pregnancy Humans Female Machine Learning Animals Prenatal Care Physicians Disease Models, Animal Maternal Health
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Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity.

Molecular informatics Mar 23, 2020
Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is stil...
Machine Learning Algorithms Computer Simulation Drug Discovery Mitochondria Drug-Related Side Effects and Adverse Reactions Databases, Chemical Quantitative Structure-Activity Relationship
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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

International journal of molecular sciences Mar 19, 2020
Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is c...
Chemical and Drug Induced Liver Injury Models, Theoretical Drug-Related Side Effects and Adverse Reactions Quantitative Structure-Activity Relationship Humans Databases, Factual Machine Learning Computer Simulation Algorithms Prognosis
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Leveraging Contextual Information in Extracting Long Distance Relations from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Relation extraction from biomedical text is important for clinical decision support applications. In post-marketing pharmacovigilance, for example, Adverse Drug Events (ADE) relate medical problems to the drugs that caused them and were the focus of ...
Datasets as Topic Product Surveillance, Postmarketing Information Storage and Retrieval Natural Language Processing Humans Medical Records Drug-Related Side Effects and Adverse Reactions Deep Learning Pharmacovigilance
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Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of derivi...
Drug Interactions Semantics Algorithms Drug-Related Side Effects and Adverse Reactions Polypharmacy Pharmacovigilance Humans Computational Biology Data Visualization Neural Networks, Computer Models, Biological
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