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

摘要

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.