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Name segmentation using hidden Markov models and its application in record linkage.

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    |November 13, 2014
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    This summary is machine-generated.

    Hidden Markov models (HMM) were tested for segmenting Brazilian names in health databases. HMM segmentation did not improve record linkage effectiveness compared to traditional methods.

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    Area of Science:

    • Health Informatics
    • Data Science
    • Biostatistics

    Background:

    • Accurate record linkage is crucial for health data analysis.
    • Traditional name segmentation methods can be labor-intensive and error-prone.
    • Evaluating novel computational approaches for name segmentation is essential.

    Purpose of the Study:

    • To assess the efficacy of Hidden Markov Models (HMM) for segmenting Brazilian person names.
    • To determine the impact of HMM-based name segmentation on health record linkage.
    • To compare HMM performance against traditional segmentation techniques.

    Main Methods:

    • Applied HMM to segment patient and mother names across Mortality Information System (SIM), Information Subsystem for High Complexity Procedures (APAC), and Hospital Information System (AIH) databases.
    • Segmented a sample of 200 patients per database using HMM.
    • Compared HMM segmentation results with manual segmentation by authors.
    • Linked APAC-SIM and APAC-AIH databases using three segmentation strategies, including HMM.

    Main Results:

    • HMM segmentation conformity ranged from 90.5% to 92.5%.
    • No significant difference in record linkage outcomes was observed between HMM and traditional segmentation strategies.
    • HMM-based segmentation did not demonstrate superior performance in this context.

    Conclusions:

    • HMM segmentation of Brazilian names is not more effective than traditional methods for record linkage.
    • Current HMM application shows limited advantage for improving health record linkage accuracy in Brazilian databases.
    • Further research may explore optimized HMM architectures or alternative segmentation algorithms.