Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs)
varying from pharmacological, immunological and genetic factors to ethnic, age, gender …

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

Background The digitization of healthcare data, resulting from the increasingly widespread
adoption of electronic health records, has greatly facilitated its analysis by computational …

The enormous amounts of data that are generated in the healthcare process and stored in
electronic health record (EHR) systems are an underutilized resource that, with the use of …

Abstract Background Adverse drug events (ADEs) as well as other preventable adverse
events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in …

Background The limited availability of clinical texts for Natural Language Processing
purposes is hindering the progress of the field. This article investigates the use of synthetic …

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 …

This article describes information retrieval, natural language processing and text mining of
electronic patient record text, also called clinical text. Clinical text is written by physicians …

We give an algorithm to learn constant-depth polynomial-size circuits augmented with
majority gates under the uniform distribution using random examples only. For circuits which …

Using longitudinal data in electronic health records (EHRs) for post-marketing adverse drug
event (ADE) detection allows for monitoring patients throughout their medical history …