DOI QR코드

DOI QR Code

Korean Standard Classification of Functioning, Disability and Health (KCF) Code Linking on Natural Language with Extract Algorithm

자연어 알고리즘을 활용한 한국표준건강분류(KCF) 코드 검색

  • Nyeon-Sik Choi (Department of Mechanical Convergence Engineering, Silla University) ;
  • Ju-Min Song (Department of Mechanical Convergence Engineering, Silla University)
  • 최년식 (신라대학교 물리치료학과) ;
  • 송주민 (신라대학교 물리치료학과)
  • Received : 2023.01.30
  • Accepted : 2023.02.09
  • Published : 2023.02.28

Abstract

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.

Keywords

References

  1. Organization WH. Towards a common language for functioning, disability, and health: ICF. The international classification of functioning, disability and health. 2002.
  2. Bioelectrical Impedance Analysis at Popliteal Regions of Human Body using BIMS. Sensors. 2016;25(1):1-7. https://doi.org/10.5369/JSST.2016.25.1.1
  3. Cieza A, Brockow T, Ewert T, et al. Linking health-status measurements to the international classification of functioning, disability and health. J Rehabil Med. 2002;34(5):205-10. https://doi.org/10.1080/165019702760279189
  4. Stucki G, Cieza A, Ewert T, et al. Application of the international classification of functioning, disability and health (icf) in clinical practice. Disabil Rehabi. 2002; 24(5):281-2. https://doi.org/10.1080/09638280110105222
  5. ustun TB, Chatterji S, Bickenbach J, et al. The International Classification of Functioning, Disability and Health: a new tool for understanding disability and health. Disab Rehab. 2003;25(11-12):565-71. https://doi.org/10.1080/0963828031000137063
  6. Imrie R. Demystifying disability: a review of the International Classification of Functioning, Disability and Health. Sociol Health Illn. 2004;26(3):287-305. https://doi.org/10.1111/j.1467-9566.2004.00391.x
  7. McDougall J, Wright V, Rosenbaum P. The ICF model of functioning and disability: incorporating quality of life and human development. Develop neurorehab. 2010;13(3):204-11. https://doi.org/10.3109/17518421003620525
  8. Fox MH, Krahn GL, Sinclair LB, et al. Using the international classification of functioning, disability and health to expand understanding of paralysis in the United States through improved surveillance. Disab Health J. 2015;8(3):457-63. https://doi.org/10.1016/j.dhjo.2015.03.002
  9. Stucki G. International Classification of Functioning, Disability, and Health (ICF): a promising framework and classification for rehabilitation medicine. Am J Phys Med Rehabiln. 2005;84(10):733-40. https://doi.org/10.1097/01.phm.0000179521.70639.83
  10. Jelsma J. Use of the International Classification of Functioning, Disability and Health: a literature survey. J Rehabil Med. 2009;41(1):1-12. https://doi.org/10.2340/16501977-0300
  11. Hand DJ, Adams NM. Data mining. Wiley StatsRef: Statistics Reference Online. 2014:1-7.
  12. Chen M-S, Han J, Yu PS. Data mining: an overview from a database perspective Trans Knowl Data Eng. 1996;8(6):866-83. https://doi.org/10.1109/69.553155
  13. Minsky M. A framework for representing knowledge. de Gruyter. Berlin, Boston. 2019.
  14. Manning C, Schutze H. Foundations of statistical natural language processing. MIT press. 1999.
  15. Hoyles C, Noss R, Adamson R. Rethinking the microworld idea. J. Educ Comput Res 2002;27(1):29-53. https://doi.org/10.2190/U6X9-0M6H-MU1Q-V36X
  16. Mitchell R, Michalski J, Carbonell T. An artificial intelligence approach. Springer. 2013.
  17. Indurkhya N, Damerau FJ. Handbook of natural language processing. CRC Press. 2010.
  18. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. Journal of the American Medical Informatics Association. 2011;18(5): 544-51. https://doi.org/10.1136/amiajnl-2011-000464
  19. Thamhain HJ. Management of technology: Managing effectively in technology-intensive organizations. John Wiley & Sons. 2005.
  20. Newman-Griffis D, Maldonado JC, Ho PS, et al. Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing. Front Rehabil Sci. 2021;2.
  21. Cieza A, Geyh S, Chatterji S, et al. ICF linking rules: an update based on lessons learned. J Rehabil Med. 2005;37(4):212-8. https://doi.org/10.1080/16501970510040263
  22. Haveliwala TH. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. Trans Knowl Data Eng. 2003;15(4):784-96. https://doi.org/10.1109/TKDE.2003.1208999