G. Niklas Norén
Roland Orre
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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Index Terms
- A hit-miss model for duplicate detection in the WHO drug safety database
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Reviews
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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