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Journal articles on the topic 'Record linkage'

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Published: 4 June 2021

Last updated: 10 March 2023

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1

Roos, L. L., and A. Wajda. "Record Linkage Strategies." Methods of Information in Medicine 30, no. 02 (1991): 117–23. http://dx.doi.org/10.1055/s-0038-1634828.

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AbstractRecord linkage techniques can help identify the same patient for matching diverse files (hospital discharge abstracts, insurance claims, registries, Vital Statistics data) which contain similar identifiers. Prior knowledge of whether a linkage is feasible is important to prevent wasted effort (additional data collection or data manipulation), which decreases the cost-effectiveness of the linkage. Using examples generated by linking the Manitoba Health Services Commission data with Vital Statistics files, a method of estimating the information in each data set is presented first. Further, the feasibility of several different record linkage strategies is described and tested, given varying amounts of information. At the margin, relatively small amounts of information (having just one more variable to match with) can make a great difference. Probabilistic linkage’s great advantage was found in those situations where only a moderate amount of extra information was available.By using the above techniques when working with one or both files in a proposed record linkage project, a much more informed judgement can now be made as to whether a linkage will or will not work. In facilitating record linkage, flexibility of both software and the strategy for matching is very important.

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2

Stern, Robert S. "Record Linkage." Archives of Dermatology 122, no. 12 (December 1, 1986): 1383. http://dx.doi.org/10.1001/archderm.1986.01660240047012.

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3

Herzog, Thomas H., Fritz Scheuren, and William E. Winkler. "Record linkage." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 5 (July 20, 2010): 535–43. http://dx.doi.org/10.1002/wics.108.

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4

Shah, Gulzar H., Kaveepan Lertwachara, and Anteneh Ayanso. "Record Linkage in Healthcare." International Journal of Healthcare Delivery Reform Initiatives 2, no. 3 (July 2010): 29–47. http://dx.doi.org/10.4018/jhdri.2010070104.

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Recent years have witnessed the development of new record linkage technologies that are increasingly being used for data integration in various application settings. The authors’ objective in this article is to provide a review of recent developments in medical record linkage and their implications in healthcare research and public health policies. In particular, the authors assess the key advantages and possible limitations of record linkage techniques and technologies in various health care scenarios where different pieces of patient records are collected and managed by different agencies. First, the authors provide a brief overview of deterministic, probabilistic, and unsupervised record linkage techniques and their advantages and limitations. Then, the authors describe current probablistic record linkage software and their functionalities, and present specific cases where probabilistic linkage has been successfully used to enhance decision-making in healthcare delivery as well as in healthcare-related public policy making. Finally, the authors outline some of the critical issues and challenges of integrating medical records across distributed databases, including technical considerations as well as concerns about patient privacy and confidentiality.

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Guiver, Tenniel, Sean Randall, Anna Ferrante, James Semmens, Phil Anderson, Teresa Dickinson, and James Boyd. "A Simple Sampling Method for Estimating the Accuracy of Large Scale Record Linkage Projects." Methods of Information in Medicine 55, no. 03 (2016): 276–83. http://dx.doi.org/10.3414/me15-01-0152.

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SummaryBackground: Record linkage techniques allow different data collections to be brought together to provide a wider picture of the health status of individuals. Ensuring high linkage quality is important to guarantee the quality and integrity of research. Current methods for measuring linkage quality typically focus on precision (the proportion of incorrect links), given the difficulty of measuring the proportion of false negatives.Objectives: The aim of this work is to introduce and evaluate a sampling based method to estimate both precision and recall following record linkage.Methods: In the sampling based method, record-pairs from each threshold (including those below the identified cut-off for acceptance) are sampled and clerically reviewed. These results are then applied to the entire set of record-pairs, providing estimates of false positives and false negatives. This method was evaluated on a synthetically generated dataset, where the true match status (which records belonged to the same person) was known.Results: The sampled estimates of linkage quality were relatively close to actual linkage quality metrics calculated for the whole synthetic dataset. The precision and recall measures for seven reviewers were very consistent with little variation in the clerical assessment results (overall agreement using the Fleiss Kappa statistics was 0.601).Conclusions: This method presents as a possible means of accurately estimating matching quality and refining linkages in population level linkage studies. The sampling approach is especially important for large project linkages where the number of record pairs produced may be very large often running into millions.

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6

Friedman, Gary D. "Automated record linkage." Clinical Pharmacology and Therapeutics 47, no. 4 (April 1990): 547–48. http://dx.doi.org/10.1038/clpt.1990.71.

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7

Sayers, Adrian, Yoav Ben-Shlomo, Ashley W. Blom, and Fiona Steele. "Probabilistic record linkage." International Journal of Epidemiology 45, no. 3 (December 20, 2015): 954–64. http://dx.doi.org/10.1093/ije/dyv322.

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8

Gruenheid, Anja, Xin Luna Dong, and Divesh Srivastava. "Incremental record linkage." Proceedings of the VLDB Endowment 7, no. 9 (May 2014): 697–708. http://dx.doi.org/10.14778/2732939.2732943.

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9

Zhang, Yinghao, Senlin Xu, Mingfan Zheng, and Xinran Li. "Field Weights Computation for Probabilistic Record Linkage in Presence of Missing Data." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 14 (April 30, 2020): 2059046. http://dx.doi.org/10.1142/s0218001420590466.

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Record linkage is the task for identifying which records refer to the same entity. When records in different data sources do not have a common key and they contain typographical errors in their identifier fields, the extended Fellegi–Sunter probabilistic record linkage method with consideration of field similarity proposed by Winkler, is one of the most effective methods to perform record linkage to our knowledge. But this method has a limitation that it cannot efficiently handle the problem of missing value in the fields, an inappropriate weight is assigned to record pair containing missing data. Therefore, to improve the performance of Winkler’s probabilistic record linkage method in presence of missing value, we proposed a solution for adjusting record pair’s weight when missing data occurred, which allows enhancing the accuracy of the Winkler’s record linkage decisions without increasing much more computational time.

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Tittarelli, A., G. Tagliabue, A. Maghini, S. Fabiano, P. Crosignani, R. Tessandori, and P. Contiero. "The EpiLink Record Linkage Software." Methods of Information in Medicine 44, no. 01 (2005): 66–71. http://dx.doi.org/10.1055/s-0038-1633924.

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Summary Objectives: Record linkage, the process of bringing together separately compiled but related records from different databases, is essential in many areas of biomedical research. We developed a record linkage program (EpiLink), which employs a simple mathematical approach. We describe the program and present results obtained testing it in a linkage task. Methods: EpiLink was designed to be flexible with user-friendly settings to tailor linkage and operating parameters to specific linkage tasks, and employ deterministic, probabilistic or sequential deterministic-probabilistic linkage strategies as required. The user can also standardize data format, examine linkage results and accept or discard them. We used EpiLink to link a subset of cases of the Lombardy Cancer Registry (20,724 records) with the Social Security file of the population (1,021,846 records) covered by the registry. The linkage strategy was deterministic, followed by several probabilistic linkage steps. Results: Manual inspection of the results showed that EpiLink achieved 98.8% specificity and 96.5% sensitivity. Conclusions: EpiLink is a practical and accurate means of linking records from different databases that can be used by non-statisticians and is efficient in terms of human and financial resources.

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Elezaj, Ogerta, and Gloria Tuxhari. "Record Linkage using Probabilistic Methods and Data Mining Techniques." Mediterranean Journal of Social Sciences 8, no. 3 (May 24, 2017): 203–7. http://dx.doi.org/10.5901/mjss.2017.v8n3p203.

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Abstract Nowadays corporations and organizations acquire large amounts of information daily which is stored in many large databases (DB). These databases mostly are heterogeneous and the data are represented differently. Data in these DB may simply be inaccurate and there is a need to clean these DB. The record linkage process is considered to be part of the data cleaning phase when working with big scale surveys considered as a data mining step. Record linkage is an important process in data integration, which consists in finding duplication records and finding matched records too. This process can be divided in two main steps Exact Record Linkage, which founds all the exact matches between two records and Probabilistic Record Linkage, which matches records that are not exactly equal but have a high probability of being equal. In recent years, the record linkage becomes an important process in data mining task. As the databases are becoming more and more complex, finding matching records is a crucial task. Comparing each possible pair of records in large DB is impossible via manual/automatic procedures. Therefore, special algorithms (blocking methods) have to be used to reduce computational complexity of comparison space among records. The paper will discuss the deterministic and probabilistic methods used for record linkage. Also, different supervised and unsupervised techniques will be discussed. Results of a real world datasets linkage (Albanian Population and Housing Census 2011 and farmers list registered by Food Safety and Veterinary Institute) will be presented.

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Nguyen, Long, Mark Stoové, Douglas Boyle, Denton Callander, Hamish McManus, Jason Asselin, Rebecca Guy, Basil Donovan, Margaret Hellard, and Carol El-Hayek. "Privacy-Preserving Record Linkage of Deidentified Records Within a Public Health Surveillance System: Evaluation Study." Journal of Medical Internet Research 22, no. 6 (June 24, 2020): e16757. http://dx.doi.org/10.2196/16757.

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Background The Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS) was established to monitor national testing and test outcomes for blood-borne viruses (BBVs) and sexually transmissible infections (STIs) in key populations. ACCESS extracts deidentified data from sentinel health services that include general practice, sexual health, and infectious disease clinics, as well as public and private laboratories that conduct a large volume of BBV/STI testing. An important attribute of ACCESS is the ability to accurately link individual-level records within and between the participating sites, as this enables the system to produce reliable epidemiological measures. Objective The aim of this study was to evaluate the use of GRHANITE software in ACCESS to extract and link deidentified data from participating clinics and laboratories. GRHANITE generates irreversible hashed linkage keys based on patient-identifying data captured in the patient electronic medical records (EMRs) at the site. The algorithms to produce the data linkage keys use probabilistic linkage principles to account for variability and completeness of the underlying patient identifiers, producing up to four linkage key types per EMR. Errors in the linkage process can arise from imperfect or missing identifiers, impacting the system’s integrity. Therefore, it is important to evaluate the quality of the linkages created and evaluate the outcome of the linkage for ongoing public health surveillance. Methods Although ACCESS data are deidentified, we created two gold-standard datasets where the true match status could be confirmed in order to compare against record linkage results arising from different approaches of the GRHANITE Linkage Tool. We reported sensitivity, specificity, and positive and negative predictive values where possible and estimated specificity by comparing a history of HIV and hepatitis C antibody results for linked EMRs. Results Sensitivity ranged from 96% to 100%, and specificity was 100% when applying the GRHANITE Linkage Tool to a small gold-standard dataset of 3700 clinical medical records. Medical records in this dataset contained a very high level of data completeness by having the name, date of birth, post code, and Medicare number available for use in record linkage. In a larger gold-standard dataset containing 86,538 medical records across clinics and pathology services, with a lower level of data completeness, sensitivity ranged from 94% to 95% and estimated specificity ranged from 91% to 99% in 4 of the 6 different record linkage approaches. Conclusions This study’s findings suggest that the GRHANITE Linkage Tool can be used to link deidentified patient records accurately and can be confidently used for public health surveillance in systems such as ACCESS.

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13

Copas, J. B., and F. J. Hilton. "Record Linkage: Statistical Models for Matching Computer Records." Journal of the Royal Statistical Society. Series A (Statistics in Society) 153, no. 3 (1990): 287. http://dx.doi.org/10.2307/2982975.

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14

Somasekhar, G., SeshaSravani K, Keerthi P, and Sai Sandeep G. "Record linkage and deduplication using traditional blocking." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 294. http://dx.doi.org/10.14419/ijet.v7i1.1.9705.

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Record Linkage and Deduplication are the two process that are used in matching records. Matching of records is done to remove the duplicate records. These duplicate records highly influence the outputs of data mining and data processing. If the matching of records is done on the single database, it is called Deduplication. In Deduplication we check for the duplicate records in the single database. Unlike deduplication if the matching of the records is done on the several databases it is called as record linkage. In this paper we also discuss about the indexing technique called as traditional blocking which is used to remove non matching pairs that leads to the less number of record pair to be compared.

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15

Ruggles, Steven, Catherine A. Fitch, and Evan Roberts. "Historical Census Record Linkage." Annual Review of Sociology 44, no. 1 (July 30, 2018): 19–37. http://dx.doi.org/10.1146/annurev-soc-073117-041447.

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For the past 80 years, social scientists have been linking historical censuses across time to study economic and geographic mobility. In recent decades, the quantity of historical census record linkage has exploded, owing largely to the advent of new machine-readable data created by genealogical organizations. Investigators are examining economic and geographic mobility across multiple generations and also engaging many new topics. Several analysts are exploring the effects of early-life socioeconomic conditions, environmental exposures, or natural disasters on family, health, and economic outcomes in later life. Other studies exploit natural experiments to gauge the impact of policy interventions such as social welfare programs and educational reforms. The new data sources have led to a proliferation of record linkage methodologies, and some widespread approaches inadvertently introduce errors that can lead to false inferences. A new generation of large-scale shared data infrastructure now in preparation will ameliorate weaknesses of current linkage methods.

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Yakout, Mohamed, Ahmed K. Elmagarmid, Hazem Elmeleegy, Mourad Ouzzani, and Alan Qi. "Behavior based record linkage." Proceedings of the VLDB Endowment 3, no. 1-2 (September 2010): 439–48. http://dx.doi.org/10.14778/1920841.1920899.

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Gomatam, Shanti, and Michael D. Larsen. "Record Linkage and Counterterrorism." CHANCE 17, no. 1 (January 2004): 25–29. http://dx.doi.org/10.1080/09332480.2004.10554883.

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Rao, Fang-Yu, Jianneng Cao, Elisa Bertino, and Murat Kantarcioglu. "Hybrid Private Record Linkage." ACM Transactions on Privacy and Security 22, no. 3 (July 19, 2019): 1–36. http://dx.doi.org/10.1145/3318462.

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19

Winkler, William E. "Matching and record linkage." Wiley Interdisciplinary Reviews: Computational Statistics 6, no. 5 (July 2, 2014): 313–25. http://dx.doi.org/10.1002/wics.1317.

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20

Verykios, Vassilios S., and Peter Christen. "Privacy-preserving record linkage." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 5 (August 20, 2013): 321–32. http://dx.doi.org/10.1002/widm.1101.

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21

Tromp, Miranda, Anita C. Ravelli, Gouke J. Bonsel, Arie Hasman, and Johannes B. Reitsma. "Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage." Journal of Clinical Epidemiology 64, no. 5 (May 2011): 565–72. http://dx.doi.org/10.1016/j.jclinepi.2010.05.008.

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Bennett, T. D., J. M. Dean, H. T. Keenan, M. H. McGlincy, A. M. Thomas, and L. J. Cook. "Linked Records of Children with Traumatic Brain Injury." Methods of Information in Medicine 54, no. 04 (2015): 328–37. http://dx.doi.org/10.3414/me14-01-0093.

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SummaryObjective: Record linkage may create powerful datasets with which investigators can conduct comparative effectiveness studies evaluating the impact of tests or interventions on health. All linkages of health care data files to date have used protected health information (PHI) in their linkage variables. A technique to link datasets without using PHI would be advantageous both to preserve privacy and to increase the number of potential linkages.Methods: We applied probabilistic linkage to records of injured children in the National Trauma Data Bank (NTDB, N = 156,357) and the Pediatric Health Information Systems (PHIS, N = 104,049) databases from 2007 to 2010. 49 match variables without PHI were used, many of them administrative variables and indicators for procedures recorded as International Classification of Diseases, 9th revision, Clinical Modification codes. We validated the accuracy of the linkage using identified data from a single center that submits to both databases.Results: We accurately linked the PHIS and NTDB records for 69% of children with any injury, and 88% of those with severe traumatic brain injury eligible for a study of intervention effectiveness (positive predictive value of 98%, specificity of 99.99%). Accurate linkage was associated with longer lengths of stay, more severe injuries, and multiple injuries.Conclusion: In populations with substantial illness or injury severity, accurate record linkage may be possible in the absence of PHI. This methodology may enable linkages and, in turn, comparative effectiveness studies that would be unlikely or impossible otherwise.

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Schmidtmann, I., and H. Brenner. "Effects of Record Linkage Errors on Disease Registration." Methods of Information in Medicine 37, no. 01 (1998): 69–74. http://dx.doi.org/10.1055/s-0038-1634498.

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Abstract:Reliable record linkage is a prerequisite for high-quality population-based disease registration. Rapid developments in computer processing have made record linkage both more efficient and more reliable in recent years. At the same time, concerns about confidentiality increasingly hinder record linkage in many disease registries. This paper provides basic algebraic models describing the effects of record linkage errors on monitoring disease incidence. Homonym errors, that is, erroneous linkage of records that pertain to distinct individuals, lead to underestimation of incidence in the registry population. The degree of underestimation strongly depends on the discriminating power of personal identifiers and the record linkage procedure on the one hand, and the number of registered cases on the other hand. Synonym errors, that is, failure to link notifications on the same individual, lead to overestimation of incidence in the population base. The combined effects of record linkage errors are illustrated with empirical examples. We conclude that it is the largest and most informative disease registries that are potentially affected most by impediments of record linkage procedures due to unduly restrictive confidentiality rules.

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Ong, Toan C., Lindsey M. Duca, Michael G. Kahn, and Tessa L. Crume. "A hybrid approach to record linkage using a combination of deterministic and probabilistic methodology." Journal of the American Medical Informatics Association 27, no. 4 (February 12, 2020): 505–13. http://dx.doi.org/10.1093/jamia/ocz232.

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Abstract Objective The disjointed healthcare system and the nonexistence of a universal patient identifier across systems necessitates accurate record linkage (RL). We aim to describe the implementation and evaluation of a hybrid record linkage method in a statewide surveillance system for congenital heart disease. Materials and Methods Clear-text personally identifiable information on individuals in the Colorado Congenital Heart Disease surveillance system was obtained from 5 electronic health record and medical claims data sources. Two deterministic methods and 1 probabilistic RL method using first name, last name, social security number, date of birth, and house number were initially implemented independently and then sequentially in a hybrid approach to assess RL performance. Results 16 480 nonunique individuals with congenital heart disease were ascertained. Deterministic linkage methods, when performed independently, yielded 4505 linked pairs (consisting of 2 records linked together within or across data sources). Probabilistic RL, using 3 initial characters of last name and gender for blocking, yielded 6294 linked pairs when executed independently. Using a hybrid linkage routine resulted in 6451 linkages and an additional 18%–24% correct linked pairs as compared to the independent methods. A hybrid linkage routine resulted in higher recall and F-measure scores compared to probabilistic and deterministic methods performed independently. Discussion The hybrid approach resulted in increased linkage accuracy and identified pairs of linked record that would have otherwise been missed when using any independent linkage technique. Conclusion When performing RL within and across disparate data sources, the hybrid RL routine outperformed independent deterministic and probabilistic methods.

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Langley, J. D., and J. C. Alsop. "Determining First Admissions in a Hospital Discharge File via Record Linkage." Methods of Information in Medicine 37, no. 01 (1998): 32–37. http://dx.doi.org/10.1055/s-0038-1634496.

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Abstract:The aim of this study was to identify first admissions in a public hospital discharge file with the greatest possible accuracy. Computerised data linkage was used to link injury events. This involved the use of “internal” data linkage (unduplication) which, in data linkage terms, is equivalent to matching two identical files. Admission status indicators obtained from deterministic and probabilistic linkages were compared with those obtained from a manual review. Small absolute reductions in error were obtained using a probabilistic linkage over a deterministic linkage. However, these reflected large relative reductions in error. A validity check confirmed initial results and discounted against possible bias due to the subjective nature of the probabilistic linking procedure.

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Mamun, Abdullah-Al, Robert Aseltine, and Sanguthevar Rajasekaran. "Efficient Record Linkage Algorithms Using Complete Linkage Clustering." PLOS ONE 11, no. 4 (April 28, 2016): e0154446. http://dx.doi.org/10.1371/journal.pone.0154446.

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Chipperfield, James O., and Raymond L. Chambers. "Using the Bootstrap to Account for Linkage Errors when Analysing Probabilistically Linked Categorical Data." Journal of Official Statistics 31, no. 3 (September 1, 2015): 397–414. http://dx.doi.org/10.1515/jos-2015-0024.

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Abstract Record linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. Record linkage is not an error-free process and can lead to linking a pair of records that do not belong to the same unit. This occurs because linking fields on the files, which ideally would uniquely identify each unit, are often imperfect. There has been an explosion of record linkage applications, particularly involving government agencies and in the field of health, yet there has been little work on making correct inference using such linked files. Naively treating a linked file as if it were linked without errors can lead to biased inferences. This article develops a method of making inferences for cross tabulated variables when record linkage is not an error-free process. In particular, it develops a parametric bootstrap approach to estimation which can accommodate the sophisticated probabilistic record linkage techniques that are widely used in practice (e.g., 1-1 linkage). The article demonstrates the effectiveness of this method in a simulation and in a real application.

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Stühlinger, W., and W. Oberaigner. "Record Linkage in the Cancer Registry of Tyrol, Austria." Methods of Information in Medicine 44, no. 05 (2005): 626–30. http://dx.doi.org/10.1055/s-0038-1634018.

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Summary Objective: Record linkage of patient data originating from various data sources and record linkage for checking uniqueness of patient registration are common tasks for every cancer registry. In Austria, there is no unique person identifier in use in the medical system. Hence, it was necessary and the goal of this work to develop an efficient means of record linkage for use in cancer registries in Austria. Methods: We adapted the method of probabilistic record linkage to the situation of cancer registries in Austria. In addition to the customary components of this method, we also took into consideration typing errors commonly occurring in names and dates of birth. The method was implemented in a program written in DELPHITM with interfaces optimised for cancer registries. Results: Applying our record linkage method to 130,509 linkages results in 105,272 (80.7%) identical pairs. For these identical pairs, 88.9% of decisions were performed automatically and 11.1% semi-automatically. For results decided automatically, 6.9% did not have simultaneous identity of last name, first name and date of birth. For results decided semi-automatically, 48.4% did not have an identical last name, 25.6% did not have an identical date of birth and 83.1% did not have simultaneous identity of last name and date of birth and first name. Conclusions: The method implemented in our cancer registry solves all record linkage problems in Austria with sufficient precision.

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Harvey, Charles, and Edmund Green. "Record Linkage Algorithms: Efficiency, Selection and Relative Confidence." History and Computing 6, no. 3 (October 1994): 143–52. http://dx.doi.org/10.3366/hac.1994.6.3.143.

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Using the case material of the Westminster Historical Database, this article describes how 30 record linkage algorithms were evaluated to select the optimum algorithm for linking poll book data. It stresses the importance of relative confidence in linkage algorithms, and shows how more discriminating algorithms may increase both confidence in linked records and rates of record linkage.

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Stergachis, Andy S. "Record Linkage Studies for Postmarketing Drug Surveillance: Data Quality and Validity Considerations." Drug Intelligence & Clinical Pharmacy 22, no. 2 (February 1988): 157–61. http://dx.doi.org/10.1177/106002808802200216.

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Large automated databases are the source of information for many record linkage studies, including postmarketing drug surveillance. Despite this reliance on prerecorded data, there have been few attempts to assess data quality and validity. This article presents some of the basic data quality and validity issues in applying record linkage methods to postmarketing surveillance. Studies based on prerecorded data, as in most record linkage studies, have all the inherent problems of the data from which they are derived. Sources of threats to the validity of record linkage studies include the completeness of data, the ability to accurately identify and follow the records of individuals through time and place, and the validity of data. This article also describes techniques for evaluating data quality and validity. Postmarketing surveillance could benefit from more attention to identifying and solving the problems associated with record linkage studies.

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Machado, Carla Jorge. "A literature review of record linkage procedures focusing on infant health outcomes." Cadernos de Saúde Pública 20, no. 2 (April 2004): 362–71. http://dx.doi.org/10.1590/s0102-311x2004000200003.

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Record linkage is a powerful tool in assembling information from different data sources and has been used by a number of public health researchers. In this review, we provide an overview of the record linkage methodologies, focusing particularly on probabilistic record linkage. We then stress the purposes and research applications of linking records by focusing on studies of infant health outcomes based on large data sets, and provide a critical review of the studies in Brazil.

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Armstrong, Bruce K., and Anne Kricker. "Record linkage -a vision renewed." Australian and New Zealand Journal of Public Health 23, no. 5 (October 1999): 451–52. http://dx.doi.org/10.1111/j.1467-842x.1999.tb01296.x.

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Verykios, Vassilios S., Alexandros Karakasidis, and Vassilios K. Mitrogiannis. "Privacy preserving record linkage approaches." International Journal of Data Mining, Modelling and Management 1, no. 2 (2009): 206. http://dx.doi.org/10.1504/ijdmmm.2009.026076.

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Faich, Gerald A. "Record linkage for postmarketing surveillance." Clinical Pharmacology and Therapeutics 46, no. 4 (October 1989): 479–80. http://dx.doi.org/10.1038/clpt.1989.169.

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Black, Donald W. "The Iowa Record-Linkage Experience." Suicide and Life-Threatening Behavior 19, no. 1 (March 1989): 78–89. http://dx.doi.org/10.1111/j.1943-278x.1989.tb00367.x.

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Black, Donald W. "The Iowa Record-Linkage Study." Archives of General Psychiatry 42, no. 1 (January 1, 1985): 71. http://dx.doi.org/10.1001/archpsyc.1985.01790240073007.

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Black, Donald W. "The Iowa Record-Linkage Study." Archives of General Psychiatry 42, no. 1 (January 1, 1985): 78. http://dx.doi.org/10.1001/archpsyc.1985.01790240080008.

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Black, Donald W. "The Iowa Record-Linkage Study." Archives of General Psychiatry 42, no. 1 (January 1, 1985): 82. http://dx.doi.org/10.1001/archpsyc.1985.01790240084009.

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Abril, Daniel, Guillermo Navarro-Arribas, and Vicenç Torra. "Choquet integral for record linkage." Annals of Operations Research 195, no. 1 (October 5, 2011): 97–110. http://dx.doi.org/10.1007/s10479-011-0989-x.

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Rentsch, Christopher T., Chodziwadziwa Whiteson Kabudula, Jason Catlett, David Beckles, Richard Machemba, Baltazar Mtenga, Nkosinathi Masilela, et al. "Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data." Gates Open Research 1 (November 6, 2017): 8. http://dx.doi.org/10.12688/gatesopenres.12751.1.

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Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

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Rentsch, Christopher T., Chodziwadziwa Whiteson Kabudula, Jason Catlett, David Beckles, Richard Machemba, Baltazar Mtenga, Nkosinathi Masilela, et al. "Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data." Gates Open Research 1 (January 11, 2018): 8. http://dx.doi.org/10.12688/gatesopenres.12751.2.

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Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

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Greiver, Michelle, Karen Tu, Steven Bernard, Babak Aliarzadeh, Sumeet Kalia, Conrad Pow, Tao Chen, and Rahim Moineddin. "Data on Patient Record Trajectory for Linkage (DataPRinT Linkage)." Canadian Journal of Diabetes 45, no. 7 (November 2021): S22. http://dx.doi.org/10.1016/j.jcjd.2021.09.065.

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Nowak, Robert, Wiktor Franus, Jiarui Zhang, Yue Zhu, Xin Tian, Zhouxian Zhang, Xu Chen, and Xiaoyu Liu. "Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles." Applied Sciences 11, no. 18 (September 10, 2021): 8417. http://dx.doi.org/10.3390/app11188417.

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We present an algorithm to find corresponding authors of patents and scientific articles. The authors are given as records in Scopus and the Chinese Patents Database. This issue is known as the record linkage problem, defined as finding and linking individual records from separate databases that refer to the same real-world entity. The presented solution is based on a record linkage framework combined with text feature extraction and machine learning techniques. The main challenges were low data quality, lack of common record identifiers, and a limited number of other attributes shared by both data sources. Matching based solely on an exact comparison of authors’ names does not solve the records linking problem because many Chinese authors share the same full name. Moreover, the English spelling of Chinese names is not standardized in the analyzed data. Three ideas on how to extend attribute sets and improve record linkage quality were proposed: (1) fuzzy matching of names, (2) comparison of abstracts of patents and articles, (3) comparison of scientists’ main research areas calculated using all metadata available. The presented solution was evaluated in terms of matching quality and complexity on ≈250,000 record pairs linked by human experts. The results of numerical experiments show that the proposed strategies increase the quality of record linkage compared to typical solutions.

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Borg, A., K. Pommerening, and M. Sariyar. "Evaluation of Record Linkage Methods for Iterative Insertions." Methods of Information in Medicine 48, no. 05 (2009): 429–37. http://dx.doi.org/10.3414/me9238.

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Summary Objectives: There have been many developments and applications of mathematical methods in the context of record linkage as one area of interdisciplinary research efforts. However, comparative evaluations of record linkage methods are still underrepresented. In this paper improvements of the Fellegi-Sunter model are compared with other elaborated classification methods in order to direct further research endeavors to the most promising methodologies. Methods: The task of linking records can be viewed as a special form of object identification. We consider several non-stochastic methods and procedures for the record linkage task in addition to the Fellegi-Sunter model and perform an empirical evaluation on artificial and real data in the context of iterative insertions. This evaluation provides a deeper insight into empirical similarities and differences between different modelling frames of the record linkage problem. In addition, the effects of using string comparators on the performance of different matching algorithms are evaluated. Results: Our central results show that stochastic record linkage based on the principle of the EM algorithm exhibits best classification results when calibrating data are structurally different to validation data. Bagging, boosting together with support vector machines are best classification methods when calibrating and validation data have no major structural differences. Conclusions: The most promising methodologies for record linkage in environments similar to the one considered in this paper seem to be stochastic ones.

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Harvey, Charles, Edmund M. Green, and Penelope J. Corfield. "Record Linkage Theory and Practice: an Experiment in the Application of Multiple Pass Linkage Algorithms." History and Computing 8, no. 2 (June 1996): 78–89. http://dx.doi.org/10.3366/hac.1996.8.2.78.

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By implementing multiple-pass record linkage algorithms between two compatible datasets, the number of records linked may be dramatically increased above the level achieved by any single algorithm. This increase is made without commensurate diminution in confidence in the linked dataset.

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Douglas, M. M., D. Gardner, D. Hucker, and S. W. Kendrick. "Best-Link Matching of Scottish Health Data Sets." Methods of Information in Medicine 37, no. 01 (1998): 64–68. http://dx.doi.org/10.1055/s-0038-1634494.

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Abstract:Methods are described used to link the Community Health Index and the National Health Service Central Register (NHSCR) in Scotland to provide a basis for a national patient index. The linkage used a combination of deterministic and probability matching techniques. A best-link principle was used by which each Community Health Index record was allowed to link only to the NHSCR record with which it achieved the highest match weight. This strategy, applied in the context of two files which each covered virtually the entire population of Scotland, increased the accuracy of linkage approximately a thousand-fold compared with the likely results of a less structured probability matching approach. By this means, 98.8% of linkable records were linked automatically with a sufficient degree of confidence for administrative purposes.

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Woogh, Carolyn M. "An Experience in Psychiatric Record Linkage." Canadian Journal of Psychiatry 33, no. 2 (March 1988): 134–39. http://dx.doi.org/10.1177/070674378803300212.

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This paper describes a personal experience in setting up a psychiatric record linkage system in an Eastern Ontario city. It discusses the rationale, background and methodology of the Kingston Psychiatric Record Linkage System and includes a detailed description of the practical issues encountered in its establishment and operation. The issues include funding, cooperation of local facilities, system start-up, operating costs, and data collection, linkage, entry, processing, storage and analysis. The limitations and uses of this system are discussed. Potential problems such as financial support and reliance on others have been resolved and the system has been functioning remarkably well since 1984. Published reports from other record linkage systems do not include such mundane details which would have been useful to know before planning this project. It is hoped that this paper will be beneficial to others who are interested in record linkage.

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Li, Wing-Ning, Donald Hayes, Jonathan Baran, Cameron Porter, and Tom Schweiger. "A Grid and Cloud Based System for Data Grouping Computation and Online Service." International Journal of Grid and High Performance Computing 3, no. 4 (October 2011): 39–52. http://dx.doi.org/10.4018/jghpc.2011100104.

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Record linkage deals with finding records that identify the same real world entity, such as an individual or a business, from a given file or set of files. Record linkage problem is also referred to as the entity resolution or record recognition problem. To locate those records identifying the same real world entity, in principle, pairwise record analyses have to be performed among all records. Analytical operations between two records vary from comparing corresponding fields to enhancing records through large knowledge bases and querying large databases. Hence, these operations are complex and take time. To reduce the number of pairwise record comparisons, blocking techniques are introduced to partition the records into blocks. After that records in each block are analyzed against one and another. One of the effective blocking methods is the closure approach, where a “related” equivalence relation is used to partition the records into equivalence classes. This paper introduces the closure problem and describes the design and implementation of a parallel and distributed closure prototype system running in an enterprise grid.

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Rezaei Ghahroodi, Zahra, and Zhina Aghamohamadi. "Record Linkage with Machine Learning Methods." Journal of Statistical Sciences 16, no. 1 (September 1, 2022): 1–24. http://dx.doi.org/10.52547/jss.16.1.1.

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KUBOTA, Kiyoshi. "Databases in Asia and Record-Linkage." Japanese Journal of Pharmacoepidemiology/Yakuzai ekigaku 16, no. 1 (2011): 27–35. http://dx.doi.org/10.3820/jjpe.16.27.

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