Evaluating automated approaches to anaphylaxis case classification using unstructured data from the FDA Sentinel System

Introduction: In May 2008, the Food and Drug Administration launched the Sentinel Initiative, a multi-year program for the establishment of a national electronic monitoring system for medical product safety that led, in 2016, to the launch of the full Sentinel System. Under the Mini-Sentinel pilot, several algorithms for identifying health outcomes of interest, including one for anaphylaxis, were developed and evaluated using data available from the Sentinel common data model. Purpose: To evaluate whether features extracted from unstructured narrative data using natural language processing (NLP) could be used to classify anaphylaxis cases. Methods: Using previously developed methods, we extracted features from unstructured narrative data using NLP and applied rule-based and similarity-based algorithms to identify anaphylaxis among 62 potential cases previously classified by human experts as anaphylaxis (N = 33), not anaphylaxis (N = 27), and unknown (N = 2). Results: The rule-based and similarity-based approaches demonstrated almost equal performance (recall 100% vs 100%, precision 60.3% vs 57.4%, F-measure: 0.753 vs 0.729). Reasons for misclassification included the inability of the algorithms to make the same clinical judgments as human experts about the timing, severity, or presence of alternative explanations; and the identification of terms consistent with anaphylaxis but present in conditions other than anaphylaxis. Conclusions: Although precision needs to be improved before these algorithms could be used without human review, we demonstrated that applying rule-based and similarity-based algorithms to unstructured narrative information from clinical records can be used for classification of anaphylaxis in the Sentinel System. Further development and assessment of these methods in the Sentinel System are warranted.