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Applying Methods for Signal Detection in Spontaneous Reports to Electronic Patient Records
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2013 (English)In: Proceedings of the  19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery (ACM), 2013Conference paper, Published paper (Refereed)
Abstract [en]

Currently, pharmacovigilance relies mainly on disproportionality analysis of spontaneous reports. However, the analysis of spontaneous reports is concerned with several problems, such as reliability, under-reporting and insucient patient information. Longitudinal healthcare data, such as Electronic Patient Records (EPRs) in which comprehensive information of each patient is covered, is a complementary source of information to detect Adverse Drug Events (ADEs). A wide set of disproportionality methods has been developed for analyzing spontaneous reports to assess the risk of reported events being ADEs. This study aims to investigate the use of such methods for detecting ADEs when analyzing EPRs. The data used in this study was extracted from Stockholm EPR Corpus. Four disproportionality methods (proportional reporting rate, reporting odds ratio, Bayesian condence propagation neural network, and Gamma-Poisson shrinker) were applied in two dierent ways to analyze EPRs: creating pseudo spontaneous reports based on all observed drug-event pairs (event-level analysis) or analyzing distinct patients who experienced a drug-event pair (patient-level analysis). The methods were evaluated in a case study on safety surveillance of Celecoxib. The results showed that, among the top 200 signals, more ADEs were detected by the event-level analysis than by the patient-level analysis. Moreover, the event-level analysis also resulted in a higher mean average precision. The main conclusion of this study is that the way in which the disproportionality analysis is applied, the event-level or patient-level analysis, can have a much higher impact on the performance than which disproportionality method is employed.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2013.
Keyword [en]
Pharmacovigilance, disproportionality analysis, drug safety, adverse drug events, electronic patient records
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-97202ISBN: 978-1-4503-2174-7 (print)OAI: oai:DiVA.org:su-97202DiVA: diva2:676246
Conference
19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD13), August 11-14, 2013, Chicago, Illinois, USA
Note

The paper was presented at the KDD 2013 - Workshop on Data Mining for Healthcare (DMH), August 11, 2013.

 https://sites.google.com/site/dmh2013ichi/home 

Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2016-02-10

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