• Aleena Banerji, Kenneth H Lai, Yu Li, Rebecca R Saff, Carlos A Camargo, Kimberly G Blumenthal, and Li Zhou.
• Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, Mass; Harvard Medical School, Boston, Mass. Electronic address: abanerji@partners.org.
• J Allergy Clin Immunol Pract. 2020 Mar 1; 8 (3): 1032-1038.e1.

BackgroundAllergic drug reaction epidemiologic data are sparse because it remains difficult to identify true cases in large data sets using manual chart review.ObjectiveTo develop and validate a novel informatics method based on natural language processing (NLP) in combination with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes that identifies allergic drug reactions in the electronic health record.MethodsPreviously studied and high-yield ICD-9-CM codes were used to screen for possible allergic drug reactions among all inpatients admitted in 2007 and 2008. A random sample was selected for manual chart review to identify true cases of allergic drug reactions. A rule-based NLP algorithm was then developed to identify allergic drug reactions using free-text clinical notes and discharge summaries from the filtered cases. The performance of using manual chart review of ICD-9-CM codes alone was compared with ICD-9-CM codes in combination with NLP.ResultsOf 3907 cases identified by ICD-9-CM codes, 725 (19%) were randomly selected for manual chart review; 335 were confirmed as allergic drug reactions, resulting in a positive predictive value (PPV) of 46% (range: 18%-79%) when using ICD-9-CM codes alone. Our NLP algorithm in combination with ICD-9-CM codes achieved a PPV of 86% (range: 69%-100%). Among the 335 confirmed positive cases, NLP identified 259 true cases, resulting in a recall/sensitivity of 77% (range: 26%-100%). Among the 390 negative cases, NLP achieved a specificity of 89% (range: 69%-100%).ConclusionUsing NLP with ICD-9-CM codes improved identification of allergic drug reactions. The resulting decrease in manual chart review effort will facilitate large epidemiology studies of this understudied area.Copyright © 2019 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

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