DOI QR코드

DOI QR Code

A Preliminary Study on the Construction of Clinical Data for Korean Herbal Prescription Recommendations for Anxiety, Depression, Anger, and Insomnia

불안, 우울, 분노 및 불면 증상에 대한 한의학파 처방 추천 임상 데이터 구축을 위한 기초 연구

  • Dong-Hoon Kang (Department of Oriental Neuropsychiatry, College of Korean Medicine, Daejeon University) ;
  • Ju-Yeon Kim (Department of Oriental Neuropsychiatry, College of Korean Medicine, Daejeon University) ;
  • Ji-Yoon Lee (Department of Oriental Neuropsychiatry, College of Korean Medicine, Daejeon University) ;
  • Je-Hyun Kim (Clinical Trial Center, Daejeon Korean Medicine Hospital of Daejeon University) ;
  • Sangjun Yea (KM Data Vision, Korea Institute of Oriental Medicine) ;
  • Ho Jang (KM Data Vision, Korea Institute of Oriental Medicine) ;
  • Sanghun Lee (KM Data Vision, Korea Institute of Oriental Medicine) ;
  • In Chul Jung (Department of Oriental Neuropsychiatry, College of Korean Medicine, Daejeon University)
  • 강동훈 (대전대학교 한의과대학 한방신경정신과학교실) ;
  • 김주연 (대전대학교 한의과대학 한방신경정신과학교실) ;
  • 이지윤 (대전대학교 한의과대학 한방신경정신과학교실) ;
  • 김제현 (대전대학교 대전한방병원 임상시험센터) ;
  • 예상준 (한국한의학연구원 한의약데이터부) ;
  • 장호 (한국한의학연구원 한의약데이터부) ;
  • 이상훈 (한국한의학연구원 한의약데이터부) ;
  • 정인철 (대전대학교 한의과대학 한방신경정신과학교실)
  • Received : 2024.08.23
  • Accepted : 2024.09.21
  • Published : 2024.09.30

Abstract

Objectives: To build basic clinical data for developing an artificial intelligence algorithm for Korean herbal prescriptions for anxiety, depression, anger, and insomnia. Methods: Subjects were recruited among those who reported mild or more severe symptoms of anxiety, depression, anger, and insomnia (Anxiety: State-Trait Anxiety Inventory≥40, Depression: Beck Depression Inventory≥14, Anger: State-Trait Anxiety Inventory≥16, Insomnia: Insomnia Severity Index≥8). Clinical observation items including basic medical information and symptoms were collected from them. These data were then analyzed by experts in Hyungsang medicine, Sasang constitutional medicine, and Sanghan-Geumgwe medicine. Results and Conclusions: Experts of the three societies presented key clinical data and recommended prescriptions. Results of this study can be used as basic data for developing an artificial intelligence algorithm for Korean herbal prescriptions in the future.

Keywords

Acknowledgement

This research is supported by grants from Korea Institute of Oriental Medicine [KSN1923111].

References

  1. Jeon JH, Lee KC. Top 10 Key Standardization and Perspectives on Artificial Intelligence in Medicine. Electronics and Telecommunications Trends. 2020;35(2):1-16. https://doi.org/10.22648/ETRI.2020.J.350201 
  2. Nensa F, Demircioglu A, Rischpler C. Artificial Intelligence in Nuclear Medicine. Journal of nuclear medicine. 2019;60(Suppl 2):29S-37S. https://doi.org/10.2967/jnumed.118.220590 
  3. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020;132(20):379-94. https://doi.org/10.1097/ALN.0000000000002960 
  4. Keskinbora K, Guven F. Artificial Intelligence and Ophthalmology. Turkish journal of ophthalmology. 2020;50(1):37-43. https://doi.org/10.4274/tjo.galenos.2020.78989 
  5. Seo JB. The Role of medical doctor in the era of artificial intelligence. Journal of the Korean Medical Association. 2019;62(3):136-39. https://doi.org/10.5124/jkma.2019.62.3.136 
  6. Park SH. Artificial Intelligence in Medicine: Beginner's Guide. Journal of the Korean Society of Radiology. 2018;78(5):301-8. https://doi.org/10.3348/jksr.2018.78.5.301 
  7. Hwang JJ, Heo MS. Future perspectives of artificial intelligence. The Journal of the Korean Dental Association. 2022;60(5):290-8. https://doi.org/10.22974/jkda.2022.60.5.004 
  8. Constantiou ID, Kallinikos J. New games, new rules: big data and the changing context of strategy. Journal of Information Technology. 2015;30(1):44-57. https://doi.org/10.1057/jit.2014.17 
  9. Kim SK, Lee SH, Kim TH, Kim A, Jang YJ, Lee SH. Construction of Prescription Support System Based on Korean Medicine Ontology. Journal of Knowledge Information Technology and Systems. 2020;15(4):561-71. https://doi.org/10.34163/JKITS.2020.15.4.011 
  10. Yea SJ, Lee SH, Jang H. Process Design and System Implementation for Building Machine Learning Data to Suggest Prescriptions by Korean Medicine School. Journal of Knowledge Information Technology and Systems. 2021;16(5):1091-102. http://dx.doi.org/10.34163/jkits.2021.16.5.020 
  11. Lee S, Yea SJ, Jang H, Lee YJ, Park JE, Han C, Lee JY. Comparative Study on Frequent Disease Patterns and Prescriptions by Three Societies of Korean Medicine for Gastrointestinal Disease. Journal of Sasang Constitutional Medicine. 2020;32(2):33-47. https://doi.org/10.7730/JSCM.2020.32.2.33 
  12. Yeo IS. The Pathology of Korean Medicine. Journal of Physiology & Pathology in Korean Medicine. 1995;4(1):35-41. 
  13. National Center for Mental Health. National Mental Health Survey. National Center for Mental Health. 2021;58-61:116-7. 
  14. Kim JW, Shin HK, Chu CN, Lee JW, Park SJ, Kim KH, Seo JH. A Clinical Study on Outpatients in Oriental Neuropsychiatry Clinic of an Oriental Medicine Hospital. Journal of Oriental Neuropsychiatry. 2007;18(3):123-34. 
  15. Jung IC, Lee SR. Clinical Review of 127 Neuropsychiatric Inpatients. Journal of Oriental Neuropsychiatry. 1999;7(2):509-30. 
  16. Seo YM, Kim SJ. Sleep and Anger. Sleep Medicine and Psychophysiology. 2019;26(2):67-74. https://doi.org/10.14401/KASMED.2019.26.2.67 
  17. Zhang H, Ni W, Li J, Zhang J. Artificial Intelligence-Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study. JMIR Medical Informatics. 2020;8(6):e17608. https://doi.org/10.2196/17608 
  18. Kim YS, Kim EJ, Lim SW, Shin DW, Oh KS, Shin YC. Association of Self-Reported Job Stress with Depression and Anxiety. Anxiety and Mood. 2015;11(1):38-46. 
  19. Kim SK, Jang HC, Kim JH, Yea SJ, Kim C, Eom DM, Song MY. A Study on Reasoning based on Herb and Formula Ontologies. Journal of Korean Medical Classics. 2009;22(3):97-106. 
  20. Kim SK, Jang HC, Kim JH, Oh YT, Kim C, Yea SJ, Song MY. Traditional Korean Medicine Diagnosis System Based on Basic Ontology. Journal of Physiology & Pathology in Korean Medicine. 2010;24(6):1111-6. 
  21. Jung TY, Kim HY, Park JH. Study on a Methodology for Developing Shanghanlun Ontology. Journal of Physiology & Pathology in Korean Medicine. 2011;25(5):765-72. 
  22. Seo JS, Kim SK, Oh YT, Kim AN, Jang HC. Web based System for Supporting Medical Treatment in Korean Medicine based on Korean Medicine Ontology. Journal of Physiology & Pathology in Korean Medicine. 2014;28(1):113-21. 
  23. The Korean Hyungsang Medicine. Collection of Clinical Experiences in Hyungsang Medicine. Jisan Publishing. 2006:3-4. 
  24. Park IS. Overview of Sasang Constitutional Medicine. Journal of Sasang Constitutional Medicine. 1989;1(1):3-12. 
  25. Korean Medical Association of Clinical Sanghan-Geumgwe. https://kmediacs.com/about/. [Accessed Jun 01, 2024]. 
  26. Chaudhary S, Wong HK, Chen Y, Zhang S, Li CR. Sex differences in the effects of individual anxiety state on regional responses to negative emotional scenes. Preprint. Res Sq. 2023;rs.3.rs-3701951. https://doi.org/10.21203/rs.3.rs-3701951/v1 
  27. Chaudhary S, Hu S, Hu K, Dominguez JC, Chao HH, Li CR. Sex differences in the effects of trait anxiety and age on resting-state functional connectivities of the amygdala. J Affect Disord Rep. 2023;14:100646. https://doi.org/10.1016/j.jadr.2023.100646 
  28. Hammen CL, Padesky CA. Sex differences in the expression of depressive responses on the Beck Depression Inventory. J Abnorm Psychol. 1977;86(6):609-614. https://doi.org/10.1037//0021-843x.86.6.609 
  29. Spielberger CD. Staxi-2 : State-Trait Anger Expression Inventory-2 : Professional Manual. Psychological Assessment Resources. 1999. 
  30. Boer J, Hohle N, Rosenblum L, Fietze I. Impact of Gender on Insomnia. Brain Sci. 2023;13(3):480. https://doi.org/10.3390/brainsci13030480 
  31. The Society of Sasang Constitutional Medicine. Sasang (Four) costitutional medicine patterns Clinical Practice Guideline of Korean Medicine. National Institute for Korean Medicine Development. 2022:76-128. 
  32. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu P, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Dean J. Scalable and accurate deep learning with electronic health records. npj Digital Medicine. 2018;1:18. https://doi.org/10.1038/s41746-018-0029-1 
  33. Miao S, Dong X, Zhang X. Jing S, Zhang X, Xu T, Wang L, Du X, Xu H, Liu Y. Detecting pioglitazone use and risk of cardiovascular events using electronic health record data in a large cohort of Chinese patients with type 2 diabetes. Journal of Diabetes. 2019;11(8):684-9. https://doi.org/10.1111/1753-0407.12894 
  34. The Society of Korean Medicine. Standard Korean Medicine Terminology. https://cis.kiom.re.kr/terminology/search.do. [Accessed Jun 01, 2024]. 
  35. Jalal S, Nicolaou S, Parker W. Artificial Intelligence, Radiology, and the Way Forward. Canadian Association Radiologists Journal. 2019;70(1):10-2. https://doi.org/10.1016/j.carj.2018.09.004 
  36. Erickson BJ, Korfiatis P, Akkus Z, Kilne TL. Machine Learning for Medical Imaging. Radiographics. 2017; 37(2):505-15. https://doi.org/10.1148/rg.2017160130 
  37. Lim YK. Pulse Diagnosis (Atlas of Clinical Diagnosis 3). Jeongdam publishing. 2003:53-9. 
  38. Befu S, Serada K. Tougue Diagnosis for Clinicians - Intergrating Oriental and Western Medicines. Koonja Publishing. 2007:157-68. 
  39. Yeom YH, Choi MH. Clinical Abdominal Examination of Korean Medicine. Euibang Publishing. 2007:24-26.