ArticleJournal of the American Medical Informatics Association : JAMIA
Efficient sequential and parallel algorithms for record linkage.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A fast privacy-preserving patient record linkage of time series data.Scientific reports · 2023Article
- Synthetic data in health care: A narrative review.PLOS digital health · 2023Article
- On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort.IEEE journal of biomedical and health informatics · 2018Article
- Article
- RLT-S: A Web System for Record Linkage.PloS one · 2015Article
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Authors and funding
4 authors.
Funding
Abstract
BACKGROUND AND
objectiveIntegrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms.
methodsOur algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components.
resultsOur sequential and parallel algorithms have been tested on a real dataset of 1,083,878 records and synthetic datasets ranging in size from 50,000 to 9,000,000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes).
conclusionsWe have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.
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