Name segmentation using hidden Markov models and its application in record linkage.
This study aimed to evaluate the use of hidden Markov models (HMM) for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient's and mother's names in the databases of the Mortality Information System (SIM), Information Subsystem for High Complexity Procedures (APAC), and Hospital Information System (AIH). A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.
Rita de Cassia Braga
Rde C
Gonçalves
Sergio Miranda
SM
Freire
D001938:Brazil; D003628:Database Management Systems; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D008498:Medical Record Linkage / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*