AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records.
Scientific reports
Nov 27, 2017
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from curr...
SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.
Artificial intelligence in medicine
Oct 27, 2017
With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., dr...
Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China.
Scientific reports
Oct 23, 2017
With increased usage of cardiovascular drugs (CVDs) for treating cardiovascular diseases, it is important to analyze CVD-associated adverse events (AEs). In this study, we systematically collected package insert-reported AEs associated with CVDs used...
Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect.
Scientific reports
Oct 9, 2017
Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation - called FDA 'category C' drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacol...
Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records.
Journal of healthcare engineering
Sep 26, 2017
Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and int...
Pattern Recognition, Automated
Software
Algorithms
Adverse Drug Reaction Reporting Systems
Electronic Health Records
Medical Informatics
Linear Models
Humans
Drug-Related Side Effects and Adverse Reactions
Language
Knowledge Bases
Natural Language Processing
Information Storage and Retrieval
Supervised Machine Learning
Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews.
Journal of healthcare engineering
Sep 5, 2017
Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. T...
Discovering associations between adverse drug events using pattern structures and ontologies.
Journal of biomedical semantics
Aug 22, 2017
BACKGROUND: Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgro...
Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases.
Scientific reports
Jun 16, 2017
Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging...
Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.
International journal of medical informatics
May 11, 2017
OBJECTIVES: Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the qu...
DrugClust: A machine learning approach for drugs side effects prediction.
Computational biology and chemistry
Mar 30, 2017
BACKGROUND: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predi...
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