Advancing the state of the art in automatic extraction of adverse drug events from narratives.

Abstract

Adverse drug events (ADEs), defined as “any injuries resulting from medication use, including physical harm, mental harm, or loss of function,”1 are reported to account for approximately 30% of all adverse events,2 with results that can include repeated hospital admission and fatality. Information about causes of ADEs can be found in data that document concurrent use of multiple medications, drug interactions, and possible allergies such as “charts, laboratory [data], prescription data” and “administrative data.”3 However, much of the crucial information related to ADEs are detailed in free text narratives and are not easily accessible by computerized systems, requiring manual review and manual identification of this information. Natural language processing (NLP) holds potential for automatically extracting ADE-related information from narratives, to make it available for decision support systems that can alert clinicians to potential ADEs at the point of care. To assess and advance the state of the art in NLP for extraction of ADEs, the National NLP Clinical Challenges (n2c2) shared task in 2018 included a track on this topic.4 This track required the identification of potential ADE mentions, along with their link to the medication that caused them, and the administration details such as the dosage, route, and frequency information related to the medication causing the ADE. The systems that tackled extraction of ADEs and related concepts primarily utilized recurrent deep neural networks consisting of bidirectional long short-term memory units, achieving performances that reached 94% in F-measure. In linking ADEs to their causes, the systems were more diverse in their methods, utilizing a range of machine learning approaches including both deep learning and more traditional methods and achieving performances that reached 96% in F-measure. These results indicate that while they are not perfect, NLP systems can successfully extract ADE information from narratives with impressive accuracy. In this editorial, we highlight 4 systems. Others are summarized in Henry et al.4