Application of exact ODDS for partial agreements of names in record linkage

Fair, Martha E.; Lalonde, Pierre; Newcombe, Howard B.

doi:10.1016/0010-4809(91)90013-m

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…16 Although this may allow greater accuracy, the relative frequency or infrequency of different names, changes due to marriage, potential variations in spelling, nicknames, abbreviations, etc, greatly increase the complexity of matching. Numerous clever approaches can be programmed, 55 but human pattern recognition is particularly hard to replicate in this area. 5 In practice, confidentiality restrictions usually do not allow the use of names in large medical databases.…”

Section: Practical and Theoretical Issues Preprocessingmentioning

confidence: 99%

Practical introduction to record linkage for injury research

2004

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Section: Practical and Theoretical Issues Preprocessingmentioning

confidence: 99%

Practical introduction to record linkage for injury research

2004

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“…Linking is accomplished primarily using probabilistic techniques supplemented by deterministic linking and manual linking as a result of manual review. The probabilistic linking software used for UPDB has evolved over time, from a command line program called Automatch using probabilistic linkage techniques based on Howard Newcombe's seminal work (Fair, Lalonde, & Newcombe, 1991;Newcombe, 1969;Newcombe, Kennedy, Axford, & James, 1959) to the current linking software called QualityStage, IBM's Websphere Information Integration Solution™ family of tools and applications (IBM, Armonk, NY, USA). QualityStage draws on information theory and advanced pattern recognition features to provide the highest level of automation for standardization and matching (Duvall et al, 2012).…”

Section: Conceptual Modelmentioning

confidence: 99%

Sowing

2023

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In recent years the development of historical databases reconstructing the lives of large populations accelerated. These considerable investments of time and money have greatly expanded possibilities for new research in history, demography, sociology, economics, and other disciplines. This special issue describes the content and design of 23 important historical databases. Authors were given the freedom to discuss a range of practical and technical decisions from evaluating archival sources to crowdsourcing data entry. The most common issue is nominative record linkage, but we find different choices between semi-automatic and fully automatic linkage techniques and various approaches for connecting diverse sources. Some databases describe special problems, like linking Chinese names, handwritten text recognition or the construction of a release in IDS-format. Other databases offer detailed descriptions of sources or discuss prospects for including new datasets.

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“…In 1992, data users of virtually every persuasion were starting to investigate the importance of data linkage, in which records one data source would be linked, record for record, or patient for patient, in order to investigate a problem that would require such linkage. Such record-level linkage could be accomplished deterministically, where there existed a unique identifier that was common to two or more datasets, or probabilistically, where linkage was accomplished by matching key variables that taken together could probably match one record with another [7][8][9]. An example of where such linkage was used, and continues to be, is the so-called SEER-Medicare dataset [10].…”

Section: Data Sources and Availability In 1992mentioning

confidence: 99%

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

Sacchi¹,

Holmes

2016

Yearb Med Inform

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Summary Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

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Application of exact ODDS for partial agreements of names in record linkage

Cited by 15 publications

References 3 publications

Practical introduction to record linkage for injury research

Practical introduction to record linkage for injury research

Sowing

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

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