[HTML][HTML] Using neural attention networks to detect adverse medical events from electronic health records
Abstract The detection of Adverse Medical Events (AMEs) plays an important role in disease
management in ensuring efficient treatment delivery and quality improvement of health …
management in ensuring efficient treatment delivery and quality improvement of health …
Adverse drug event detection from electronic health records using hierarchical recurrent neural networks with dual-level embedding
Introduction Adverse drug event (ADE) detection is a vital step towards effective
pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs …
pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs …
[HTML][HTML] Extraction of information related to adverse drug events from electronic health record notes: design of an end-to-end model based on deep learning
Background: Pharmacovigilance and drug-safety surveillance are crucial for monitoring
adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug …
adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug …
Bidirectional LSTM-CRF for adverse drug event tagging in electronic health records
Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and
prevention of future incidents caused by potentially harmful ADEs. Electronic health records …
prevention of future incidents caused by potentially harmful ADEs. Electronic health records …
A deep learning based named entity recognition approach for adverse drug events identification and extraction in health social media
Drug safety surveillance plays a significant role in supporting medication decision-making
by both healthcare providers and patients. Extracting adverse drug events (ADEs) from …
by both healthcare providers and patients. Extracting adverse drug events (ADEs) from …
Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets
Objective The primary objective of this review is to investigate the effectiveness of machine
learning and deep learning methodologies in the context of extracting adverse drug events …
learning and deep learning methodologies in the context of extracting adverse drug events …
[HTML][HTML] Adverse drug reaction detection via a multihop self-attention mechanism
Background The adverse reactions that are caused by drugs are potentially life-threatening
problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their …
problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their …
Leveraging food and drug administration adverse event reports for the automated monitoring of electronic health records in a pediatric hospital
The objective of this study was to determine whether the Food and Drug Administration's
Adverse Event Reporting System (FAERS) data set could serve as the basis of automated …
Adverse Event Reporting System (FAERS) data set could serve as the basis of automated …
A study of deep learning approaches for medication and adverse drug event extraction from clinical text
Objective This article presents our approaches to extraction of medications and associated
adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 …
adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 …
Adverse drug event detection and extraction from open data: A deep learning approach
B Fan, W Fan, C Smith - Information Processing & Management, 2020 - Elsevier
Drug prescription is a task that doctors face daily with each patient. However, when
prescribing drugs, doctors must be conscious of all potential drug side effects. In fact …
prescribing drugs, doctors must be conscious of all potential drug side effects. In fact …