Automated Approaches to Anaphylaxis Case Classification Using Unstructured Data

Project Title Automated Approaches to Anaphylaxis Case Classification Using Unstructured Data
Date Posted
Wednesday, March 7, 2018
Status
Complete
Description

The objective of this workgroup was to evaluate whether features extracted from unstructured narrative data using natural language processing (NLP) could be used to classify anaphylaxis cases. Using previously developed methods, the workgroup extracted features from unstructured narrative data using NLP and applied rule- and similarity-based algorithms to identify anaphylaxis among 62 potential cases previously classified by human experts as anaphylaxis, not anaphylaxis, and unknown. 

Workgroup Leader(s)

Robert Ball MD, MPH, ScM; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, MD

Workgroup Members

Sengwee Toh ScD; Jamie Nolan BA; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA

Kevin Haynes PharmD, MSCE; Translational Research for Affordability and Quality, HealthCore, Inc., Wilmington, DE

Richard Forshee PhD; Taxiarchis Botsis PhD; Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, FDA, Silver Spring, MD

Health Outcome
anaphylaxis
Time Period
January 2009 - December 2010
Data Sources
Mini-Sentinel Distributed Database (MSDD)
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