Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records

Large-scale drug safety surveillance and pharmacovigilance are key components of effective drug regulation systems, clinical practice, and public health programs [1]. Although the efficacy and safety of a drug must be demonstrated in a series of clinical trials prior to approval [2], many adverse drug events (ADEs) are detected only after a drug has been marketed when it is used by a larger and more diverse population than during clinical trials. Adverse drug events discovered after a drug is in broad use can be a significant cause of morbidity and mortality. Thus, effective and accurate postmarket drug surveillance is in urgent demand for the protection of public health and the reduction of healthcare expenditures due to ADE-related hospital complications [3–5]. Spontaneous reporting systems [6–9] have been traditionally used for pharmacovigilance. However, this type of data is inherently passive because except for drug companies’ spontaneous reporting systems, reporting is voluntary, and studies have shown that as many as 90% of serious ADEs go unreported [10]. Electronic health records (EHRs) contain real-time real-world clinical data gathered during routine clinical care, offering a potentially more proactive approach to pharmacovigilance [2]. Therefore, EHRs for post-market surveillance play an important role in the new paradigm of drug regulation [11]. More importantly, compared with structured data or coded data in EHRs, unstructured clinical narratives provide more information on ADE documentation. A study shows that only 9020 (28.6%) out of 31,531 patients with documented statin side effects had the relevant ADE recorded in a structured format [12]. Therefore, developing advanced natural language processing (NLP) techniques to unpin ADE information from EHR narratives will greatly facilitate proactive, accurate, and efficient postmarket drug safety monitoring on a large scale. In 2010, i2b2 partnered with the VA Salt Lake City Health Care System and organized an NLP open challenge [13], which supported community efforts in applying NLP to extract medication and treat targets and caused adverse events from EHR narratives. However, the annotation schema defined for that challenge only covers a limited set of entities relevant for pharmacovigilance. To further/ better assess the current methodological progress in this research area, we organized the “NLP Challenge for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0)” in 2018, which offers larger scale, expert-annotated clinical notes labeled with more fine-grained clinically named entities and relations related to drug safety surveillance. There are 15 teams from seven countries registered in this challenge and in total 41 runs from 11 teams were submitted. Part of this theme issue of Drug Safety is to present recent advances in mining unstructured information from clinical narratives in the context of drug safety surveillance and pharmacovigilance. There are five articles from the MADE1.0 challenge, including an overview paper and four Part of a theme issue on “NLP Challenge for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0)” guest edited by Feifan Liu, Abhyuday Jagannatha and Hong Yu.

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