Detection of Pharmacovigilance-Related adverse Events Using Electronic Health Records and automated Methods

By Krystl Haerian, D. Varn, S. Vaidya, Lyudmila Ena, H. Chase, Carol Friedman. 2012

127 citations recorded by the source corpus.

Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients' underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural‐language processing (NLP) and a knowledge source to differentiate cases in which the patient's disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs: rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval: 88.9–96.7%) and 91.8% (confidence interval: 84.0–96.2%), respectively. The method resulted in considerable saving of time: for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs.

Subfields: Pediatrics, Perinatology and Child Health, Pharmacology, Toxicology

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Citations in the corpus

Mining Electronic Health Records: A Survey (2017, background): “…2015; Wang et al. 2013a; Luo et al. 2016; Hripcsak et al. 2015; Hagar et al. 2014 [Sathyanarayana et al. 2014; White et al. 2013; Iyer et al. 2014; Haerian et al. 2012; Pathak et al. 2013; Carroll et al. 2011; Xu et al. 2011a; Bobo et al. 2014; Zhang et al. 2015; Melton and Hripcsak 2005]…”
Clinical Information Extraction Applications: A Literature Review (2017, background): “MedLEE is one of the earliest clinical NLP systems developed and is mostly used for pharmacovigilance [26,74,75] and pharmacoepidemiology [76,77].”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, background): “Wang et al. [54, 55], Friedman [56], and Haerian et al. [57] used MedLEE to recognize named entities such as medications and potential adverse events (e.g., diseases and symptoms) in unstructured data from hospital records.”