Detecting adverse events using information technology.
By David W. Bates, R. Evans, H. Murff, P. Stetson, Lisa Pizziferri, George Hripcsak. JAMIA Journal of the American Medical Informatics Association, 2003
461 citations recorded by the source corpus.
Subfields: Emergency Medical Services, Health Information Management, Toxicology
View Paper: https://www.semanticscholar.org/paper/cd88a0c2d417c0041ab0bad81182bdffd02a6810
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Toxicology
Citations in the corpus
- Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, unknown): “We have adopted the notion of symbolic methods by Bates et al. [35], which includes using rules, regular expressions, and syntactic grammar to specify the patterns for the presence of an ADE as stated in the clinical narratives.”
- Electronic medical records for discovery research in rheumatoid arthritis (2010, unknown): “of adverse events (17,18), but not yet for classification of”
- Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. (2009, unknown): “39,40 However, these focus on ADE detection and patient safety, but not on knowledge discovery and pharmacovigilance.”
- Automated detection of adverse events using natural language processing of discharge summaries. (2005, unknown): “10,25 It is unclear, however, whether natural language processors can detect a wide range of complex adverse events accurately enough to assist health care institutions meaningfully.”
- Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks (2010, background): “Unfortunately, the frequency of adverse drug reactions is often under-estimated due to a reliance on voluntary reporting (Bates et al., 2003; van Der Hooft et al., 2006).”
- Detecting adverse events for patient safety research: a review of current methodologies (2003, unknown): “Advances in information technology, the increase in hospitals with integrated information systems, and ambulatory electronic medical records have increasingly made it possible for institutions to automatically detect adverse events [42].”