AIMC Topic: Pharmacovigilance
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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...
Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.
Journal of biomedical informatics
Jun 1, 2018
INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to ...
Towards precision informatics of pharmacovigilance: OAE-CTCAE mapping and OAE-based representation and analysis of adverse events in patients treated with cancer drugs.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology ...
Hybrid Semantic Analysis for Mapping Adverse Drug Reaction Mentions in Tweets to Medical Terminology.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Social networks, such as Twitter, have become important sources for active monitoring of user-reported adverse drug reactions (ADRs). Automatic extraction of ADR information can be crucial for healthcare providers, drug manufacturers, and consumers. ...
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...
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...
Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.
Yearbook of medical informatics
Nov 10, 2016
OBJECTIVES: This paper reviews work over the past two years in Natural Language Processing (NLP) applied to clinical and consumer-generated texts.
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 ...
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 ...
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