AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines.

Journal of biomedical informatics Mar 8, 2017
Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug ...
Humans Algorithms Support Vector Machine Drug-Related Side Effects and Adverse Reactions Chemical and Drug Induced Liver Injury Forecasting Drug Discovery
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Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports.

Artificial intelligence in medicine Feb 10, 2017
OBJECTIVE: Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporti...
Data Mining Humans Adverse Drug Reaction Reporting Systems Machine Learning Drug Interactions Drug-Related Side Effects and Adverse Reactions Bayes Theorem
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Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
An important aspect of post-marketing drug surveillance involves identifying potential side-effects utilizing adverse drug event (ADE) reporting systems and/or Electronic Health Records. These data are noisy, necessitating identified drug/ADE associa...
Product Surveillance, Postmarketing Models, Theoretical Support Vector Machine Drug-Related Side Effects and Adverse Reactions Electronic Health Records Humans Adverse Drug Reaction Reporting Systems ROC Curve Machine Learning
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Accuracy of an automated knowledge base for identifying drug adverse reactions.

Journal of biomedical informatics Dec 16, 2016
INTRODUCTION: Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disse...
Humans Drug-Related Side Effects and Adverse Reactions Electronic Data Processing Adverse Drug Reaction Reporting Systems Knowledge Bases Pharmacovigilance
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DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

BMC medical genomics Aug 10, 2016
BACKGROUND: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mut...
Algorithms Polymorphism, Single Nucleotide Humans Machine Learning Stochastic Processes Models, Biological Drug-Related Side Effects and Adverse Reactions
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Learning temporal weights of clinical events using variable importance.

BMC medical informatics and decision making Jul 21, 2016
BACKGROUND: Longitudinal data sources, such as electronic health records (EHRs), are very valuable for monitoring adverse drug events (ADEs). However, ADEs are heavily under-reported in EHRs. Using machine learning algorithms to automatically detect ...
Humans Machine Learning Electronic Health Records Drug-Related Side Effects and Adverse Reactions Pharmacovigilance
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Ensembles of randomized trees using diverse distributed representations of clinical events.

BMC medical informatics and decision making Jul 21, 2016
BACKGROUND: Learning deep representations of clinical events based on their distributions in electronic health records has been shown to allow for subsequent training of higher-performing predictive models compared to the use of shallow, count-based ...
Decision Trees Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Semantics Electronic Health Records Models, Theoretical Humans Machine Learning
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An ensemble method for extracting adverse drug events from social media.

Artificial intelligence in medicine Jun 6, 2016
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large dat...
Humans Natural Language Processing Pharmacovigilance Drug-Related Side Effects and Adverse Reactions Internet Social Media Semantics Data Mining
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Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events.

Journal of biomedical semantics May 21, 2016
BACKGROUND: Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-saving chemotherapy treatment.
Nervous System Diseases Drug-Related Side Effects and Adverse Reactions Biological Ontologies
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Predictive modeling of structured electronic health records for adverse drug event detection.

BMC medical informatics and decision making Nov 25, 2015
BACKGROUND: The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. Th...
Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Machine Learning Algorithms Humans Forecasting Patient Safety Databases, Factual Computer Simulation Electronic Health Records
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