Journal of Convergence Korean Medicine (대한융합한의학회지)

Academy of Convergence Korean Medicine (대한융합한의학회)
권호 표지

A Study for Diagnostic Agreement between Web-based Diagnosis Support System and Korean Medical Doctors' Diagnosis

웹기반 진단 보조 시스템의 진단 일치도 연구

  • Seungyob Yi (Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University) ;
  • Minji Kang (Research Institute of Medical Nutrition, Kyung Hee University) ;
  • Hyun Jung Lim (Research Institute of Medical Nutrition, Kyung Hee University) ;
  • WM Yang (Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University)
  • 이승엽 (경희대학교 한의과대학) ;
  • 강민지 (경희대학교 동서의학대학원 의학영양학과) ;
  • 임현정 (경희대학교 동서의학대학원 의학영양학과) ;
  • 양웅모 (경희대학교 한의과대학)
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Objectives: This study aims to evaluate the clinical validity of the system by conducting a clinical study to assess the diagnostic agreement between the system and Korean medical doctors. Methods: This study was conducted from September 7, 2023, to December 7, 2023, across five Korean medicine institutions, involving 100 adult participants aged 20-64 who consented to participate. Participants first entered their symptoms into a web-based program, which utilized an AI-based algorithm to diagnose 36 types of pattern differentiation. Subsequently, Korean medical doctors conducted face-to-face diagnoses using the same 36 types. The diagnostic agreement between the system and the doctors' diagnoses was analyzed using descriptive statistical analysis, and the results were expressed as a percentage agreement. Results: Analysis of the diagnostic data from 100 participants revealed that the web-based diagnosis support system identified an average of 7.76±0.79 patterns per patient, while Korean medical doctors identified an average of 7.99±0.10 patterns per patient. The diagnostic agreement between the system and the doctors showed an average of 7.08±1.08 patterns per patient, with an overall diagnostic agreement rate of 88.57±13.31%. Conclusion: This study developed a web-based diagnosis support system for traditional Korean medicine and evaluated its clinical validity by assessing diagnostic agreement. Comparing the diagnoses of the system with those of Korean medical doctors for 100 patients, the system showed an approximately 89% agreement rate with the clinical diagnoses. The system holds potential for aiding Korean medical doctors in pattern differentiation diagnosis in clinical practice.

Keywords

Acknowledgement

이 논문은 한국한의약진흥원을 통해 보건복지부의 지원을 받아 수행된 연구임.

References

  1. Lee IS, Cho HS, Ji GY, Lee YT, Kim JW, Jeon SH, Kim KM, Kim GC, Kim KK. A study for diagnostic correspondent rates between DSOM and korean medical doctors' diagnosis about menstrual pain. The Journal of Korean Obstetrics and Gynecology. 2015;28(3): 1-10.
  2. Choi SH. Development of web-based diagnosis expert system of traditional oriental medicine. Journal of Physiology & Pathology in Korean Medicine. 2002;16(3):528-31.
  3. Sung HK, Jung B, Kim KH, Sung SH, Park JK. Trends and future direction of the clinical decision support system in traditional Korean Medicine. Journal of pharmacopuncture. 2019;22(4):260-8.
  4. Wang S, Long S, Wu W. Access Application of Traditional Chinese Medicines as Personalized Therapy in Human Cancers. The American journal of Chinese medicine. 2018;46(05):953-70.
  5. Elf M, Flink M, Nilsson M, Tistad M, von Koch L, Ytterberg C. The case of value-based healthcare for people living with complex long-term conditions. BMC Health Services Research. 2017;17:1-6.
  6. Park KM, Park JH. A Methodology for Representation of Clinical Data in Oriental Medicine. Journal of Physiology & Pathology in Korean Medicine. 2002;16(5):845-50.
  7. Kim SK, Kim JH, Jang HC, Kim AN, Yea SJ, Kim C, Song MY. Reasoning and Learning Methods for Diagnosis in Oriental Medicine. Journal of Physiology & Pathology in Korean Medicine. 2009;23(5):942-9.
  8. Park JH, Shin SW, Jung GS, Park KM, Kim SH. Development and Evaluation of Ontology for Diagnosis in Oriental Medicine. Journal of Physiology & Pathology in Korean Medicine. 2006;20(1):202-8.
  9. Lee IS, Lee YT, Chi GY, Kim JW, Kim KK. Study for Diagnostic Correspondent Rates between DSOM and Oriental Medical Doctors. Journal of Physiology & Pathology in Korean Medicine. 2008;22(6):1359-67.
  10. Seo SW, Kim GJ, Kim NG, Seo JB, Ye JC, Jang DK, Choi BW. 의료인공지능 개발 및 사용 가이드라인 정립. Seoul:National Academy of Medicine of Korea. 2022:33-53.
  11. Kim HH, Yang SB, Kang YS, Park YB, Kim JH. Machine Learning Approach to Blood Stasis Pattern Identification Based on Self-reported Symptoms. Korean Journal of Acupuncture. 2016;33(3):102-13.