AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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An automated pipeline for analyzing medication event reports in clinical settings.

BMC medical informatics and decision making
BACKGROUND: Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medicatio...

Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach.

Molecules (Basel, Switzerland)
Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug ...

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

PLoS computational biology
Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction...

Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning.

IEEE journal of biomedical and health informatics
Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite...

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.

PloS one
BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a ...

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies.

Human heredity
AIMS: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs...

PISTON: Predicting drug indications and side effects using topic modeling and natural language processing.

Journal of biomedical informatics
The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected a...

Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach.

Journal of healthcare engineering
Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The ob...

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Journal of chemical information and modeling
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically ...

Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.

Artificial intelligence in medicine
Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals h...