Tables for Allergy NLP Matching

August 6, 2015

An accurate computable representation of food and drug allergy is essential for safe healthcare. We developed and evaluate a SQL-based method to map free-text allergy/adverse reaction entries to structured entries, using RxNorm as the target vocabulary.  The system was developed and tested using a perioperative management system using a training set of 24,599 entries and a test set of 24,857 entries from Vanderbilt University.Our goal was to develop a high performance, easily-maintained algorithm to identify medication and food allergies and sensitivities from unstructured allergy entries in electronic medical record (EHR) systems.

Accuracy, precision, recall, and F-measure for medication allergy matches were all above 98% in the training dataset and above 97% in the testing dataset for all allergy entries. Corresponding values for food allergy matches were above 97% and above 93%, respectively. Specificity for NLP drug matches was 90.3% and 85.0% for drug matches and 100% and 88.9% for food matches in the training and testing datasets, respectively.

Key Reference:
Epstein RH, St Jacques P, Stockin M, Rothman B, Ehrenfeld JM, Denny JC. Automated identification of drug and food allergies entered using non-standard terminology.  JAMIA 2013; 20:962-8

The files attached below can be mapped to the flowchart in Figure 1 of the paper.

JAMIA_NLP_Files_v101_0.xlsx