Article

A hit-miss model for duplicate detection in the WHO drug safety database

Authors:

G. Niklas Norén,

Roland Orre,

Andrew BateAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 459 - 468

https://doi.org/10.1145/1081870.1081923

Published: 21 August 2005 Publication History

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Abstract

The WHO Collaborating Centre for International Drug Monitoring in Uppsala, Sweden, maintains and analyses the world's largest database of reports on suspected adverse drug reaction incidents that occur after drugs are introduced on the market. As in other post-marketing drug safety data sets, the presence of duplicate records is an important data quality problem and the detection of duplicates in the WHO drug safety database remains a formidable challenge, especially since the reports are anonymised before submitted to the database. However, to our knowledge no work has been published on methods for duplicate detection in post-marketing drug safety data. In this paper, we propose a method for probabilistic duplicate detection based on the hit-miss model for statistical record linkage described by Copas & Hilton. We present two new generalisations of the standard hit-miss model: a hit-miss mixture model for errors in numerical record fields and a new method to handle correlated record fields. We demonstrate the effectiveness of the hit-miss model for duplicate detection in the WHO drug safety database both at identifying the most likely duplicate for a given record (94.7% accuracy) and at discriminating duplicates from random matches (63% recall with 71% precision). The proposed method allows for more efficient data cleaning in post-marketing drug safety data sets, and perhaps other applications throughout the KDD community.

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A hit-miss model for duplicate detection in the WHO drug safety database

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The WHO Collaborating Centre for International Drug Monitoring in Uppsala, Sweden, maintains and analyses the world’s largest database of reports on suspected adverse drug reaction (ADR) incidents that occur after drugs are on the market. The ...
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Reviews

Reviewer: John A. Fulcher

Data cleaning is an essential first step in the knowledge discovery in databases (KDD) process. Apart from the removal of noise, another critical preprocessing task is the removal of duplicate records from the databases in question. The application of interest to the authors is drug safety, although the techniques they describe have wider applicability. Norén and coauthors use Copas and Hilton's hit-miss model [1] for statistical record linkage within the World Health Organization's (WHO's) drug safety database. They note in passing that most of the parameters needed for this model are determined by the entire data set, which reduces the risk of overfitting. Moreover, they found that adding the following features improved the performance of the standard hit-miss model: modeling errors in numerical record fields, and incorporating a computationally efficient method of handling correlated record fields. A total of 38 groups of duplicate records had been previously (manually) identified in the WHO drug safety database. The authors' modified hit-miss model was applied retrospectively to this database. This led, first, to the identification of the most likely duplicates for a given record (with 94.7 percent accuracy), and, second, to discriminating duplicates from random matches (with 63 percent recall and 71 percent precision). In short, they claim to be able to detect a "significant proportion of duplicates without generating many false leads." The authors plan to perform a prospective study at some point in the future, using their modified hit-miss model to highlight suspected duplicates in an unlabeled data subset, following up their results with a manual review. This paper will appeal to researchers with an interest in KDD, especially in preprocessing in general, and in duplicate record elimination in particular. Online Computing Reviews Service

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Published In

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Hirai EHaruki TBaba TMiyazaki D(2023)Analyses of Dupilumab-Related Ocular Adverse Drug Reactions Using the WHO’s VigiBaseAdvances in Therapy10.1007/s12325-023-02573-340:9(3830-3856)Online publication date: 26-Jun-2023
https://doi.org/10.1007/s12325-023-02573-3
Hembrow IHembrow I(2023)From Analogue to AIRalph Edwards: RARE EVENTS10.1007/978-3-031-14981-8_16(283-297)Online publication date: 21-Jan-2023
https://doi.org/10.1007/978-3-031-14981-8_16
Xiang XDing MZong HLiu YCheng HHe CHe H(2020)The safety and feasibility of a new rehabilitation robotic exoskeleton for assisting individuals with lower extremity motor complete lesions following spinal cord injury (SCI): an observational studySpinal Cord10.1038/s41393-020-0423-9Online publication date: 7-Feb-2020
https://doi.org/10.1038/s41393-020-0423-9
Salem JYang TMoslehi JWaintraub XGandjbakhch EBachelot AHidden-Lucet FHulot JKnollmann BLebrun-Vignes BFunck-Brentano CGlazer ARoden D(2019)Androgenic Effects on Ventricular RepolarizationCirculation10.1161/CIRCULATIONAHA.119.040162140:13(1070-1080)Online publication date: 24-Sep-2019
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Arnaud LMertz PGavand PMartin TChasset FTebacher-Alt MLambert AMuller CSibilia JLebrun-Vignes BSalem J(2019)Drug-induced systemic lupus: revisiting the ever-changing spectrum of the disease using the WHO pharmacovigilance databaseAnnals of the Rheumatic Diseases10.1136/annrheumdis-2018-21459878:4(504-508)Online publication date: 4-Feb-2019
https://doi.org/10.1136/annrheumdis-2018-214598
Pimplikar RKannan KMondal AMondal JSaxena SParija GDevulapalli C(2017)RISE: Resolution of Identity Through Similarity Establishment on Unstructured Job DescriptionsService-Oriented Computing10.1007/978-3-319-69035-3_2(19-36)Online publication date: 18-Oct-2017
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Singh KGupta GShroff GAgarwal P(2017)Automated Product-Attribute MappingTrends and Applications in Knowledge Discovery and Data Mining10.1007/978-3-319-67274-8_15(163-175)Online publication date: 7-Oct-2017
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(2016)ReferencesMethodological Developments in Data Linkage10.1002/9781119072454.refs(233-252)Online publication date: 5-Feb-2016
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Karimi SWang CMetke-Jimenez AGaire RParis C(2015)Text and Data Mining Techniques in Adverse Drug Reaction DetectionACM Computing Surveys10.1145/271992047:4(1-39)Online publication date: 11-May-2015
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Tregunno PFink DFernandez-Fernandez CLázaro-Bengoa ENorén G(2014)Performance of Probabilistic Method to Detect Duplicate Individual Case Safety ReportsDrug Safety10.1007/s40264-014-0146-y37:4(249-258)Online publication date: 14-Mar-2014
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