Title: A systematic approach to 'cleaning' of drug name records data in the FAERS database: a case report

Authors: Michael A. Veronin; Robert P. Schumaker; Rohit R. Dixit; Pooja Dhake; Morgan Ogwo

Addresses: Department of Pharmaceutical Sciences, Ben and Maytee Fisch College of Pharmacy, The University of Texas at Tyler, 3900 University Blvd., Tyler, Texas 75799, USA ' Data Analytics Laboratory, Department of Computer Science, Soules College of Business, The University of Texas at Tyler, 3900 University Blvd., Tyler, Texas 75799, USA ' Department of Computer Science, Soules College of Business, The University of Texas at Tyler, 3900 University Blvd., Tyler, Texas 75799, USA ' Department of Computer Science, Soules College of Business, The University of Texas at Tyler, 3900 University Blvd., Tyler, Texas 75799, USA ' Department of Pharmaceutical Sciences, Ben and Maytee Fisch College of Pharmacy, The University of Texas at Tyler, 3900 University Blvd., Tyler, Texas 75799, USA

Abstract: Data 'cleaning', also known as data 'cleansing', or data 'curation' is about identifying and rectifying errors in data. The objective of this report is to present a data cleaning and standardisation process for the drug name files in the U.S. Food and Drug Administration adverse event reporting system database, FAERS. Drug name data was cleaned and standardised using a combination of data cleaning tools and manual correction techniques. Data files were organised into frequency intervals and a strategy of cleaning using iteration and programming scripts in the MySQL Workbench was employed. The download of the FAERS quarterly reports for the time periods ranging from Q1 2004 to Q3 2016 resulted in 32,736,657 DRUG file records. Records contained a variety of errors, such as misspellings, abbreviations and non-descript or ambiguous names. Upon completion of the process, standardisation of greater than 95% of the drug name data in the FAERS database was achieved. With large datasets such as FAERS, a cleaning process is necessary to rectify data that may be incomplete or inaccurate due to input errors, in order to improve the quality and validity of information.

Keywords: FAERS; U.S. Food and Drug Administration; FDA; adverse drug event; ADE; drug safety; data cleaning.

DOI: 10.1504/IJBDM.2020.112404

International Journal of Big Data Management, 2020 Vol.1 No.2, pp.105 - 118

Received: 23 Apr 2019
Accepted: 06 Oct 2019

Published online: 14 Jan 2021 *

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