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

Drug Safety ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 95-97 ◽  
Author(s):  
Feifan Liu ◽  
Abhyuday Jagannatha ◽  
Hong Yu
2010 ◽  
Vol 19 (5) ◽  
pp. e16-e16 ◽  
Author(s):  
K. S. Boockvar ◽  
E. E. Livote ◽  
N. Goldstein ◽  
J. R. Nebeker ◽  
A. Siu ◽  
...  

Drug Safety ◽  
2015 ◽  
Vol 38 (7) ◽  
pp. 671-682 ◽  
Author(s):  
Artur Akbarov ◽  
Evangelos Kontopantelis ◽  
Matthew Sperrin ◽  
Susan J. Stocks ◽  
Richard Williams ◽  
...  

2019 ◽  
Vol 27 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Fenia Christopoulou ◽  
Thy Thy Tran ◽  
Sunil Kumar Sahu ◽  
Makoto Miwa ◽  
Sophia Ananiadou

AbstractObjectiveIdentification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.Materials and MethodsWe proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined them using an ensemble method. The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences.ResultsOur team ranked third with a micro-averaged F1 score of 94.72% and 87.65% for relation and end-to-end relation extraction, respectively (Tracks 2 and 3). Our ensemble effectively takes advantages from our proposed models. Analysis of the reported results indicated that our proposed approach is more generalizable than the top-performing system, which employs additional training data- and corpus-driven processing techniques.ConclusionsWe proposed a relation extraction system to identify relations between drugs and medication-related entities. The proposed approach is independent of external syntactic tools. Analysis showed that by using latent Drug-Drug interactions we were able to significantly improve the performance of non–Drug-Drug pairs in EHRs.


2019 ◽  
Vol 29 (1) ◽  
pp. 52-59 ◽  
Author(s):  
A Jay Holmgren ◽  
Zoe Co ◽  
Lisa Newmark ◽  
Melissa Danforth ◽  
David Classen ◽  
...  

BackgroundElectronic health records (EHR) can improve safety via computerised physician order entry with clinical decision support, designed in part to alert providers and prevent potential adverse drug events at entry and before they reach the patient. However, early evidence suggested performance at preventing adverse drug events was mixed.MethodsWe used data from a national, longitudinal sample of 1527 hospitals in the USA from 2009 to 2016 who took a safety performance assessment test using simulated medication orders to test how well their EHR prevented medication errors with potential for patient harm. We calculated the descriptive statistics on performance on the assessment over time, by years of hospital experience with the test and across hospital characteristics. Finally, we used ordinary least squares regression to identify hospital characteristics associated with higher test performance.ResultsThe average hospital EHR system correctly prevented only 54.0% of potential adverse drug events tested on the 44-order safety performance assessment in 2009; this rose to 61.6% in 2016. Hospitals that took the assessment multiple times performed better in subsequent years than those taking the test the first time, from 55.2% in the first year of test experience to 70.3% in the eighth, suggesting efforts to participate in voluntary self-assessment and improvement may be helpful in improving medication safety performance.ConclusionHospital medication order safety performance has improved over time but is far from perfect. The specifics of EHR medication safety implementation and improvement play a key role in realising the benefits of computerising prescribing, as organisations have substantial latitude in terms of what they implement. Intentional quality improvement efforts appear to be a critical part of high safety performance and may indicate the importance of a culture of safety.


2010 ◽  
Vol 6 (2) ◽  
pp. 91-96 ◽  
Author(s):  
Tejal K. Gandhi ◽  
Andrew C. Seger ◽  
J. Marc Overhage ◽  
Michael D. Murray ◽  
Carol Hope ◽  
...  

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