Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

Currently, medical institutes generally use EMR to record patient's condition, including
diagnostic information, procedures performed, and treatment results. EMR has been …

Background Automatic clinical text classification is a natural language processing (NLP)
technology that unlocks information embedded in clinical narratives. Machine learning …

Hercules Dalianis Secondary Use of Electronic Patient Records Page 1 Hercules Dalianis
Clinical Text Mining Secondary Use of Electronic Patient Records Page 2 Clinical Text …

Introduction This work describes the Medication and Adverse Drug Events from Electronic
Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 …

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug
events (ADEs) with pharmaceutical products. This article is a comprehensive structured …

To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …

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 …

Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act
(HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service …

Electronic health records contain large amounts of longitudinal data that are valuable for
biomedical informatics research. The application of machine learning is a promising …