Semantic processing to identify adverse drug event information from black box warnings

Adverse drug events account for two million combined injuries, hospitalizations, or deaths each year. Furthermore, there are few comprehensive, up-to-date, and free sources of drug information. Clinical decision support systems may significantly mitigate the number of adverse drug events. However, these systems depend on up-to-date, comprehensive, and codified data to serve as input. The DailyMed website, a resource managed by the FDA and NLM, contains all currently approved drugs. We used a semantic natural language processing approach that successfully extracted information for adverse drug events, at-risk conditions, and susceptible populations from black box warning labels on this site. The precision, recall, and F-score were, 94%, 52%, 0.67 for adverse drug events; 80%, 53%, and 0.64 for conditions; and 95%, 44%, 0.61 for populations. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may support clinical decision support systems.