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.

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Preparation of name and address data for record linkage using hidden Markov models

BMC Medical Informatics and Decision Making ◽

10.1186/1472-6947-2-9 ◽

2002 ◽

Vol 2 (1) ◽

Cited By ~ 39

Author(s):

Tim Churches ◽

Peter Christen ◽

Kim Lim ◽

Justin Xi Zhu

Keyword(s):

Hidden Markov Models ◽

Record Linkage ◽

Markov Models ◽

Hidden Markov ◽

Address Data

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Estimating Personality Impression from Speech Record Using Hidden Markov Models

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.135.1517 ◽

2015 ◽

Vol 135 (12) ◽

pp. 1517-1523 ◽

Cited By ~ 1

Author(s):

Yicheng Jin ◽

Takuto Sakuma ◽

Shohei Kato ◽

Tsutomu Kunitachi

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov

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Hidden Markov Processes

10.23943/princeton/9780691133157.001.0001 ◽

2014 ◽

Cited By ~ 2

Author(s):

M. Vidyasagar

Keyword(s):

Hidden Markov Models ◽

Markov Processes ◽

Viterbi Algorithm ◽

Markov Models ◽

Hidden Markov ◽

Local Alignment ◽

Biological Applications ◽

Standard Material ◽

Hidden Markov Processes ◽

Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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