Abstract
Linking medical records across multiple databases is a very important task, since medical errors, uncompensated care and medical costs are rising at a rapid rate. However, inconsistencies in data records, caused primarily by errors in data entry, make matching of records and satisfactory data linkage difficult. This paper presents and assesses the performance of an approximate matching methodology developed utilising an algorithm for machine learning. The results indicate that this approach to record linkage is promising.
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