Objective: To determine whether natural language processing (NLP) can effectively detect
adverse events defined in the New York Patient Occurrence Reporting and Tracking System …

Objective: Detecting adverse events is pivotal for measuring and improving medical safety,
yet current techniques discourage routine screening. The authors hypothesized that …

Context: Although patient safety is a major problem, most health care organizations rely on
spontaneous reporting, which detects only a small minority of adverse events. As a result …

Abstract Study Objective: To evaluate the potential ability of computerized information
systems (ISs) to identify and prevent adverse events in medical patients. Design: Clinical …

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 …

Methods for surveillance of adverse events (AEs) in clinical settings are limited by cost,
technology, and appropriate data availability. In this study, two methods for semi-automated …

Context Currently most automated methods to identify patient safety occurrences rely on
administrative data codes; however, free-text searches of electronic medical records could …

Objective: To evaluate the use of a computer program to identify adverse drug events
(ADEs) in the ambulatory setting and to evaluate the relative contribution of four computer …

With the rapidly growing use of electronic health records, the possibility of large-scale
clinical information extraction has drawn much attention. We aim to extract adverse drug …

Narrative reports in medical records contain a wealth of information that may augment
structured data for managing patient information and predicting trends in diseases. Pertinent …