AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.
PloS one
Nov 21, 2018
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
Oct 22, 2018
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
Sep 27, 2018
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
Aug 19, 2018
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
Aug 15, 2018
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
Aug 6, 2018
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...
Normalizing Spontaneous Reports Into MedDRA: Some Experiments With MagiCoder.
IEEE journal of biomedical and health informatics
Jul 30, 2018
Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language...
Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges.
Pharmacotherapy
Jul 22, 2018
The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing amount of data suggesting that a large amount of patient harm is preventable and can be mitigated wit...
A meta-learning framework using representation learning to predict drug-drug interaction.
Journal of biomedical informatics
Jun 26, 2018
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.
BMC bioinformatics
Jun 13, 2018
BACKGROUND: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adve...
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