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Drug-Related Side Effects and Adverse Reactions

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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 Natural Language Processing Medical Records Information Storage and Retrieval Drug-Related Side Effects and Adverse Reactions Product Surveillance, Postmarketing Humans Pharmacovigilance Deep Learning
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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...
Computational Biology Humans Neural Networks, Computer Models, Biological Algorithms Drug Interactions Polypharmacy Drug-Related Side Effects and Adverse Reactions Data Visualization Pharmacovigilance Semantics
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Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy co...
Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Electronic Health Records Logistic Models Support Vector Machine Machine Learning Humans Databases, Factual
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Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Clinical pharmacology and therapeutics Feb 28, 2020
Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this s...
Humans Drug Evaluation, Preclinical Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Machine Learning Animals Drug Discovery
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Prediction of Side Effects Using Comprehensive Similarity Measures.

BioMed research international Feb 27, 2020
Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method th...
Drug Repositioning Area Under Curve Drug Interactions Humans Machine Learning Polymorphism, Single Nucleotide Vorinostat Clonidine Dasatinib Drug-Related Side Effects and Adverse Reactions Sitagliptin Phosphate
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Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem.

Molecules (Basel, Switzerland) Jan 23, 2020
Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming and studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are approaches, which present a cost-efficient...
Chemical and Drug Induced Liver Injury Databases, Pharmaceutical Databases, Chemical Quantitative Structure-Activity Relationship Neural Networks, Computer Computer Simulation Algorithms Liver Drug-Related Side Effects and Adverse Reactions
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Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels.

BMC bioinformatics Dec 23, 2019
BACKGROUND: Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying A...
Data Mining United States Food and Drug Administration Drug Labeling United States Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Machine Learning Deep Learning
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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation.

Chemical research in toxicology Nov 22, 2019
Drug toxicity evaluation is an essential process of drug development as it is reportedly responsible for the attrition of approximately 30% of drug candidates. The rapid increase in the number and types of large toxicology data sets together with the...
Drug-Related Side Effects and Adverse Reactions Quantitative Structure-Activity Relationship Machine Learning Animals Humans Big Data
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LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets.

Journal of chemical information and modeling Oct 9, 2019
Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster speed and lower cost compared to experimental bioassays. Gradient boosting is an effect...
Mutagens Algorithms Reproducibility of Results Drug Development Molecular Structure Drug-Related Side Effects and Adverse Reactions Machine Learning Datasets as Topic Software
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A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis.

International journal of medical informatics Oct 5, 2019
CONTEXT: Adverse events in healthcare are often collated in incident reports which contain unstructured free text. Learning from these events may improve patient safety. Natural language processing (NLP) uses computational techniques to interrogate f...
Humans Electronic Health Records Natural Language Processing Adverse Drug Reaction Reporting Systems Risk Management Drug-Related Side Effects and Adverse Reactions
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