Text mining is the computational process of extracting meaningful information from large
amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources …

Context Adverse events in healthcare are often collated in incident reports which contain
unstructured free text. Learning from these events may improve patient safety. Natural …

The paper presents an intelligent system to automatically infer trends in the public opinion
regarding the stance towards the vaccination topic: it enables the detection of significant …

With an aging patient population and increasing complexity in patient disease trajectories,
physicians are often met with complex patient histories from which clinical decisions must be …

Abstract Objectives Adverse drug reactions (ADRs) are believed to be a leading cause of
death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With …

Vaccines have contributed to dramatically decrease mortality from infectious diseases in the
20th century. However, several social discussion groups related to vaccines have emerged …

Objective: To describe and compare logistic regression and neural network modeling
strategies to predict hospital admission or transfer following initial presentation to …

Importance Accurate prediction of outcomes among patients in intensive care units (ICUs) is
important for clinical research and monitoring care quality. Most existing prediction models …

There is great interest in the application of 'artificial intelligence'(AI) to pharmacovigilance
(PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application …

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 …