AIMC Topic: 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...
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...
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...
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...
An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example.
Computational and mathematical methods in medicine
Oct 1, 2019
In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are prese...
Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats.
Topics in companion animal medicine
Sep 30, 2019
Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes including patient death. This constitutes a major public health concern. The US Food and Drug Administratio...
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability.
International journal of molecular sciences
Sep 28, 2019
The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is...
Adverse drug reaction detection via a multihop self-attention mechanism.
BMC bioinformatics
Sep 18, 2019
BACKGROUND: The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients. Detecting ADRs through clinical trials ta...
Prediction of Potential Drug-Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features.
International journal of molecular sciences
Aug 22, 2019
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and disea...
Comparison of text processing methods in social media-based signal detection.
Pharmacoepidemiology and drug safety
Aug 7, 2019
PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-bas...
Data Collection
Pharmacovigilance
Datasets as Topic
Retrospective Studies
False Negative Reactions
False Positive Reactions
Adverse Drug Reaction Reporting Systems
Sensitivity and Specificity
Natural Language Processing
Social Media
Feasibility Studies
Drug-Related Side Effects and Adverse Reactions
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