Spelling correction for the telecommunications network for the deaf
Communications of the ACM
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Automating the approximate record-matching process
Information Sciences—Informatics and Computer Science: An International Journal
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
Efficient Record Linkage in Large Data Sets
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Exploratory Data Mining and Data Cleaning
Exploratory Data Mining and Data Cleaning
Eliminating fuzzy duplicates in data warehouses
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Automatic record linkage using seeded nearest neighbour and support vector machine classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Quality and Record Linkage Techniques
Data Quality and Record Linkage Techniques
Automatic training example selection for scalable unsupervised record linkage
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Towards scalable real-time entity resolution using a similarity-aware inverted index approach
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Many problems arise when linking medical records from multiple databases. Matching these data to other data is problematic since even small errors, such as data entry errors, different text format, and missing data, can prevent the exact-match algorithms. Evidence from previous studies suggested that approximate field matching represent a solution to resolve the problem by identifying equivalent string values in different representations. The purpose of this article is to explore the effectiveness of a medical record matching method using a fuzzy logic framework. This article considers quantitative measures of the typical elements in medical records, and fuzzy logic is applied to link to the linguistic concepts. Moreover, this article discusses the medical record matching from the developed framework, which is tested on a public data set. The results from the test on a public data set indicate that the medical record matching method using fuzzy logic framework provides an effective solution for dealing with linkage problems, and illustrate that the multiple valued logic method outlined can potentially be applied to address similar problems in other databases.