Tables for Allergy NLP Matching


adverse reactions    allergies    electronic medical records    medications    nlp    NLP

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

Attachment Size
NLP Lookup tables as used in the JAMIA paper 432.65 KB