A Record Linkage Protocol for a Diabetes Registry at Ethnically Diverse Community Health Centers

Maizlish, Neil; Herrera, Linda

doi:10.1197/jamia.m1696

Cited by 13 publications

(15 citation statements)

References 10 publications

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“…Health agencies keep track of patients′ health information and at the same time records of a patient reside in multiple data sources. All the records of a patient may be needed to accurately diagnose a disease or prescribe medicine for a disease for the patient [ 1 , 2 ]. Disease evolution, drug discovery and side effects of a drug may require analysis of health records across these data sources [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Record Linkage Algorithms Using Complete Linkage Clustering

Mamun¹,

Aseltine²,

Rajasekaran³

2016

PLoS ONE

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Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times.

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Section: Introductionmentioning

confidence: 99%

Efficient Record Linkage Algorithms Using Complete Linkage Clustering

Mamun¹,

Aseltine²,

Rajasekaran³

2016

PLoS ONE

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“…Finding data of an individual across these sources requires efficient algorithms. Record linkage has also applications in disease evolution [ 3 , 4 ], master data management, copy detection in digital documents [ 5 , 6 ], historical data management, and so on.…”

Section: Introductionmentioning

confidence: 99%

RLT-S: A Web System for Record Linkage

2015

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BackgroundRecord linkage integrates records across multiple related data sources identifying duplicates and accounting for possible errors. Real life applications require efficient algorithms to merge these voluminous data sources to find out all records belonging to same individuals. Our recently devised highly efficient record linkage algorithms provide best-known solutions to this challenging problem.MethodWe have developed RLT-S, a freely available web tool, which implements our single linkage clustering algorithm for record linkage. This tool requires input data sets and a small set of configuration settings about these files to work efficiently. RLT-S employs exact match clustering, blocking on a specified attribute and single linkage based hierarchical clustering among these blocks.ResultsRLT-S is an implementation package of our sequential record linkage algorithm. It outperforms previous best-known implementations by a large margin. The tool is at least two times faster for any dataset than the previous best-known tools.ConclusionsRLT-S tool implements our record linkage algorithm that outperforms previous best-known algorithms in this area. This website also contains necessary information such as instructions, submission history, feedback, publications and some other sections to facilitate the usage of the tool.AvailabilityRLT-S is integrated into http://www.rlatools.com, which is currently serving this tool only. The tool is freely available and can be used without login. All data files used in this paper have been stored in https://github.com/abdullah009/DataRLATools. For copies of the relevant programs please see https://github.com/abdullah009/RLATools.

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“…Integration of medical records provides a great opportunity to analyze and evaluate disease evolution 12 Methods3 exist for linking records across multiple medical data centers to identify disease origin and diversity 4. Copy detection in digital documents also employs data integration techniques to detect similarities 5…”

Section: Introductionmentioning

confidence: 99%

Efficient sequential and parallel algorithms for record linkage

Mamun

Tian

Aseltine

et al. 2014

J Am Med Inform Assoc

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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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A Record Linkage Protocol for a Diabetes Registry at Ethnically Diverse Community Health Centers

Cited by 13 publications

References 10 publications

Efficient Record Linkage Algorithms Using Complete Linkage Clustering

Efficient Record Linkage Algorithms Using Complete Linkage Clustering

RLT-S: A Web System for Record Linkage

Efficient sequential and parallel algorithms for record linkage

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