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KMID : 1211620230180010077
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Journal of the Korean Society of Physical Medicine
2023 Volume.18 No. 1 p.77 ~ p.86
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Korean Standard Classification of Functioning, Disability and Health (KCF) Code Linking on Natural Language with Extract Algorithm
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Choi Nyeon-Sik
Song Ju-Min
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Abstract
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PURPOSE: This study developed an experimental algorithm, which is similar or identical to semantic linking for KCF codes, even if it converted existing semantic code linking methods to morphological code extraction methods.
The purpose of this study was to verify the applicability of the system.
METHODS: An experimental algorithm was developed as a morphological extraction method using code-specific words in the KCF code descriptions. The algorithm was designed in five stages that extracted KCF code using natural language paragraphs. For verification, 80 clinical natural language experimental cases were defined. Data acquisition for the study was conducted with the deliberation and approval of the bioethics committee of the relevant institution.
Each case was linked by experts and was extracted through the System. The linking accuracy index model was used to compare the KCF code linking by experts with those extracted from the system.
RESULTS: The accuracy was checked using the linking accuracy index model for each case. The analysis was divided into five sections using the accuracy range. The section with less than 25% was compared; the first experimental accuracy was 61.24%. In the second, the accuracy was 42.50%. The accuracy was improved to 30.59% in the section by only a weight adjustment. The accuracy can be improved by adjusting several independent variables applied to the system.
CONCLUSION: This paper suggested and verified a way to easily extract and utilize KCF codes even if they are not experts. KCF requires the system for utilization, and additional study will be needed.
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KEYWORD
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Code extraction algorithm, Code search, KCF, Linking, Natural language
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Listed journal information
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