Advanced Search

Journal of Traditional Chinese Medicine >

2025 , Vol. 45 >Issue 5: 1152 - 1163

DOI: https://doi.org/10.19852/j.cnki.jtcm.20250319.001

Original Articles

Study on subtyping and Traditional Chinese Medicine treatment of depression based on machine learning and text mining

  • Mengyue FAN ,
  • Lin YAO ,
  • Guoqing ZHANG ,
  • Ruixue WANG ,
  • Kexin CHEN ,
  • Yujing FAN ,
  • Ziming WANG ,
  • Jia FU ,
  • Yongjun CHEN ,
  • Taiyi WANG
Expand
  • 1 Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
    2 Graduate School of Informatics, Kyoto University, Kyoto 6068501, Japan

Received date: 2024-08-03

  Accepted date: 2024-12-22

  Online published: 2025-03-19

Supported by

National Key Research and Development Plan: Regulatory Pathways and Mechanisms of Conception and Governor Vessels Surface Stimulation in the Treatment of “Uterus and Brain” Disorders(2022YFC3500405);Taishan Scholar Youth Project of Shandong Province(tsqn202306188);National Natural Science Foundation of China: Epigenetic Regulation of Vascular Neural Unit Function by Vascular Endothelial Histone Deacetylase: a New Antidepressant Application and Mechanism of Huangqi Guizhi Wuwu Decoction(82274128);National Natural Science Foundation of China: Study on the Anti-depression Mechanism of Electroacupuncture based on the Regulation of Biological Clock Gene in Prefrontal Cortex(81973948);Joint Fund of Shandong Provincial Natural Science Foundation: High-Throughput Screening and Key Target Validation of Traditional Chinese Medicine Blood-Activating and Stasis-Resolving Components using a Vascular Microenvironment Simulation Chip(ZR2021LZY020);Student Research Training Program of Shandong University of Traditional Chinese Medicine: the Therapeutic Mechanism of Huangqi Guizhi Wuwu Decoction on Chronic Unpredictable Mild Stress Model Mice Based on the Endothelial Nitric Oxide Synthase-Nitric oxide Pathway Research of Vascular Endothelial Eells(202210441008)

Fold

Abstract

OBJECTIVE: To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine (TCM) experiences through artificial intelligence (AI).

METHODS: We retrieved depression-related literature published from inception to April 2023 from databases. From these sources, we extracted symptoms, signs, and prescriptions associated with depression. By utilizing the tree number system in the medical subject headings (MeSH), we established a hierarchical relationship matrix for symptoms/signs, as well as depression sample fingerprints. Using an unsupervised clustering algorithm, we constructed a machine learning model for classifying depression patients. Furthermore, we conducted an analysis of medication rules for each depression cluster.

RESULTS: We created a My Structured Query Language (MySQL) database containing datasets of depression-symptoms/signs and depression-herbs, through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression. We established hierarchical relationships among symptoms/signs of depression patients. Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes, with each subtype corresponding to a specific treatment prescription. Notably, one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs. This finding was further supported by data from Qi-deficiency patients, as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.

CONCLUSIONS: This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.

Key words： machine learning; depression; artificial intelligence; medicine, Chinese traditional; disease subtyping

Cite this article

Mengyue FAN , Lin YAO , Guoqing ZHANG , Ruixue WANG , Kexin CHEN , Yujing FAN , Ziming WANG , Jia FU , Yongjun CHEN , Taiyi WANG . Study on subtyping and Traditional Chinese Medicine treatment of depression based on machine learning and text mining[J]. Journal of Traditional Chinese Medicine, 2025 , 45(5) : 1152 -1163 . DOI: 10.19852/j.cnki.jtcm.20250319.001

References

1. Herrman H, Patel V, Kieling C, et al. Time for united action on depression: a Lancet-World Psychiatric Association Commission. Lancet 2022; 399: 957-1022.
2. Dong M, Zeng LN, Lu L, et al. Prevalence of suicide attempt in individuals with major depressive disorder: a Meta-analysis of observational surveys. Psychol Med 2019; 49: 1691-704.
3. Lee Y, Brietzke E, Cao B et al. Development and implementation of guidelines for the management of depression: a systematic review. Bull World Health Organ 2020; 98: 683-97H.
4. Tóth B, Hegyi P, Lantos T, et al. The Efficacy of saffron in the treatment of mild to moderate depression: a Meta-analysis. Planta Med 2019; 85: 24-31.
5. Ramanuj P, Ferenchick EK, Pincus HA. Depression in primary care: part 2 — management. BMJ 2019; 365: l835.
6. Cipriani A, Furukawa TA, Salanti G et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network Meta-analysis. Lancet 2018; 391: 1357-66.
7. Lewis G, Marston L, Duffy L, et al. Maintenance or discontinuation of antidepressants in primary care. N Engl J Med 2021; 385: 1257-67.
8. Leichsenring F, Steinert C, Rabung S, Ioannidis JPA. The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: an umbrella review and Meta-analytic evaluation of recent Meta-analyses. World Psychiatry 2022; 21: 133-45.
9. Zhang X, Kang D, Zhang L, Peng L. Shuganjieyu capsule for major depressive disorder (MDD) in adults: a systematic review. Aging Ment Health 2014; 18: 941-53.
10. Wang L, Fan Y, He J, et al. Efficacy and safety of Shuganjieyu Capsule alone or in combination with other antidepressants in the treatment of postpartum depression: a Meta-analysis. Evid-Based Complement Altern Med 2022; 2022: 5260235.
11. Wang XL, Feng ST, Wang YT, Zhang NN, Wang ZZ, Zhang Y. Canonical Chinese medicine formula Danzhi-Xiaoyao-San for treating depression: a systematic review and Meta-analysis. J Ethnopharmacol 2022; 287: 114960.
12. Man C, Li C, Gong D, Xu J, Fan Y. Meta-analysis of Chinese herbal Xiaoyao formula as an adjuvant treatment in relieving depression in Chinese patients. Complement Ther Med 2014; 22: 362-70.
13. Jin X, Jiang M, Gong D, Chen Y, Fan Y. Efficacy and Safety of Xiaoyao formula as an adjuvant treatment for post-stroke depression: a Meta-analysis. Explore 2018; 14: 224-9.
14. Ho D, Quake SR, McCabe ERB, et al. Enabling technologies for personalized and precision medicine. Trends Biotechnol 2020; 38: 497-518.
15. Li L, Wang Z, Wang J, Zheng Y, Li Y, Wang Q. Enlightenment about using TCM constitutions for individualized medicine and construction of Chinese-style precision medicine: research progress with TCM constitutions. Sci China Life Sci 2021; 64: 2092-9.
16. Zhou X, Li Y, Peng Y, et al. Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of Traditional Chinese Medicine. Front Med 2014; 8: 337-46.
17. Nero C, Vizzielli G, Lorusso D, et al. Patient-derived organoids and high grade serous ovarian cancer: from disease modeling to personalized medicine. J Exp Clin Cancer Res 2021; 40: 116.
18. Pamarthy S, Sabaawy HE. Patient derived organoids in prostate cancer: improving therapeutic efficacy in precision medicine. Mol Cancer 2021; 20: 125.
19. Beitler JR, Goligher EC, Schmidt M, et al. Personalized medicine for ARDS: the 2035 research agenda. Intensive Care Med 2016; 42: 756-67.
20. Wang J, Duan L, Li H, Liu J, Chen H. Construction of an artificial intelligence Traditional Chinese Medicine diagnosis and treatment model based on syndrome elements and small-sample data. Engineering 2022; 8: 29-32.
21. Pan LG, Qi CH, Shen XB, Huang YX, Yang XR, Sun XJ. Traditional Chinese Medicine syndrome analysis on oxaliplatin-induced peripheral neuropathy and clinical efficacy of Bushen Yiqi formula (补肾益气方): a prospective randomized controlled study. J Tradit Chin Med 2023; 43: 1234-42.
22. Wang Y, Shi X, Li L, Efferth T, Shang D. The impact of artificial intelligence on Traditional Chinese Medicine. Am J Chin Med 2021; 49: 1297-314.
23. Li XY, Yang YH, Sun J, et al. Effectiveness and safety of Jiawei Xiaoyao pill (加味逍遥丸) in the treatment of premenstrual syndrome (liver depression, spleen deficiency, and blood-heat syndrome): a multi-center, randomized, placebo-controlled trial. J Tradit Chin Med 2024; 44: 373-80.
24. Liu LY, Xu XP, Luo LY, et al. Brain connectomic associations with Traditional Chinese Medicine diagnostic classification of major depressive disorder: a diffusion tensor imaging study. Chin Med 2019; 14: 15.
25. Zhao A, Qiu W, Mao L, et al. The efficacy and safety of Jiedu Tongluo granules for treating post-stroke depression with Qi deficiency and blood stasis syndrome: study protocol for a randomized controlled trial. Trials 2018; 19: 275.
26. Chen Z, Cao Y, He S, Qiao Y. Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of Traditional Chinese Medicine. Chin Med 2018; 13: 12.
27. Kanawong R, Obafemi-Ajayi T, Ma T, Xu D, Li S, Duan Y. Automated tongue feature extraction for ZHENG classification in Traditional Chinese Medicine. Evid Based Complement Alternat Med 2012; 2012: 1-14.
28. Tang Y, Li Z, Yang D, et al. Research of insomnia on Traditional Chinese Medicine diagnosis and treatment based on machine learning. Chin Med 2021; 16: 2.
29. Chen H, He Y. Machine learning approaches in Traditional Chinese Medicine: a systematic review. Am J Chin Med 2022; 50: 91-131.
30. Wu X, Jiang R, Zhang MQ, Li S. Network-based global inference of human disease genes. Mol Syst Biol 2008; 4: 1-11.
31. Gan X, Shu Z, Wang X, et al. Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine. Sci Adv 2023; 9: 1-15.
32. Niu Q, Li H, Tong L, et al. TCMFP: a novel herbal formula prediction method based on network target’s score integrated with semi-supervised learning genetic algorithms. Brief Bioinform 2023; 24: bbad102.
33. Shang H, Zhang L, Xiao T, et al. Study on the differences of gut microbiota composition between phlegm-dampness syndrome and Qi-Yin deficiency syndrome in patients with metabolic syndrome. Front Endocrinol 2022; 13: 1063579.
34. Yu G. Using meshes for MeSH term enrichment and semantic analyses. Bioinformatics 2018; 34: 3766-7.
35. Guo ZH, You ZH, Huang DS, et al. MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm. Brief Bioinform 2021; 22: 2085-95.
36. Kim S, Yeganova L, Wilbur WJ. Meshable : searching PubMed abstracts by utilizing MeSH and MeSH-derived topical terms. Bioinformatics 2016; 32: 3044-6.
37. Arji G, Safdari R, Rezaeizadeh H, Abbassian A, Mokhtaran M, Hossein Ayati M. A systematic literature review and classification of knowledge discovery in traditional medicine. Comput Methods Programs Biomed 2019; 168: 39-57.
38. Deng YS, Han SY, Xi H, et al. Traditional Chinese Medicine in the treatment of recurrent respiratory tract infections in children: an overview of systematic reviews and Meta-analyses. J Tradit Chin Med 2024, 44: 871-84.
39. Song J, Liu X, Deng Q, et al. A network-based approach to investigate the pattern of syndrome in depression. Evid Based Complement Alternat Med 2015; 2015: 1-11.
40. Dou Z, Xia Y, Zhang J, et al. Syndrome differentiation and treatment regularity in Traditional Chinese Medicine for type 2 diabetes: a text mining analysis. Front Endocrinol 2021; 12: 728032.
41. Hu Q, Yu T, Li J, Yu Q, Zhu L, Gu Y. End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in Traditional Chinese Medicine. Comput Methods Programs Biomed 2019; 174: 9-15.
42. Yang H, Lee HJ. Research trend visualization by MeSH terms from PubMed. Int J Environ Res Public Health 2018; 15: 1113.
43. Karim MR, Beyan O, Zappa A, et al. Deep learning-based clustering approaches for bioinformatics. Brief Bioinform 2021; 22: 393-415.
44. Zhang Y, Kiryu H. MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes. Brief Bioinform 2022; 23: bbac372.
45. Tang JL, Liu BY, Ma KW. Traditional Chinese Medicine. The Lancet 2008; 372: 1938-40.
46. Lyu M, Fan G, Xiao G, et al. Traditional Chinese medicine in COVID-19. Acta Pharm Sin B 2021; 11: 3337-63.
47. Zhuang W, Liu SL, Xi SY, et al. Traditional Chinese Medicine decoctions and Chinese patent medicines for the treatment of depression: efficacies and mechanisms. J Ethnopharmacol 2023; 307: 116272.
48. Dai J, Fang J, Sun S, et al. ZHENG-omics application in ZHENG classification and treatment: Chinese personalized medicine. Evid-Based Complement Altern Med ECAM 2013; 2013: 235969.
49. Lu C, Zha Q, Chang A, He Y, Lu A. Pattern differentiation in Traditional Chinese Medicine can help define specific indications for biomedical therapy in the treatment of rheumatoid arthritis. J Altern Complement Med N Y N 2009; 15: 1021-5.
50. He Y, Lu A, Zha Y, Tsang I. Differential effect on symptoms treated with Traditional Chinese Medicine and western combination therapy in RA patients. Complement Ther Med 2008; 16: 206-11.
51. Wu G, Zhao J, Zhao J, et al. Exploring biological basis of syndrome differentiation in coronary heart disease patients with two distinct syndromes by integrated multi-omics and network pharmacology strategy. Chin Med 2021; 16: 109.
52. Deussing JM, Arzt E. P2X7 receptor: a potential therapeutic target for depression? Trends Mol Med 2018; 24: 736-47.
53. Cruz-Pereira JS, Rea K, Nolan YM, O’Leary OF, Dinan TG, Cryan JF. Depression’s unholy trinity: dysregulated stress, immunity, and the microbiome. Annu Rev Psychol 2020; 71: 49-78.
54. Borbély é, Simon M, Fuchs E, Wiborg O, Czéh B, Helyes Z. Novel drug developmental strategies for treatment-resistant depression. Br J Pharmacol 2022; 179: 1146-86.
55. Peci?a M, Karp JF, Mathew S, Todtenkopf MS, Ehrich EW, Zubieta JK. Endogenous opioid system dysregulation in depression: implications for new therapeutic approaches. Mol Psychiatry 2019; 24: 576-87.
56. Nguyen TD, Harder A, Xiong Y, et al. Genetic heterogeneity and subtypes of major depression. Mol Psychiatry 2022; 27: 1667-75.
57. Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The need for multi-omics biomarker signatures in precision medicine. Int J Mol Sci 2019; 20: 4781.
58. Howes OD, Thase ME, Pillinger T. Treatment resistance in psychiatry: state of the art and new directions. Mol Psychiatry 2022; 27: 58-72.
59. Zhang S, Zhao L, Wang H, et al. Efficacy of modified Liujunzi decoction on functional dyspepsia of spleen-deficiency and Qi-stagnation syndrome: a randomized controlled trial. BMC Complement Altern Med 2013; 13: 54.
60. Ha Y, Huang P, Yan Y, et al. A systematic review and Meta-analysis on a disease in TCM: astragalus injection for gathering Qi depression. Evid-Based Complement Altern Med ECAM 2020; 2020: 2803478.
61. Wang YS, Shen CY, Jiang JG. Antidepressant active ingredients from herbs and nutraceuticals used in TCM: pharmacological mechanisms and prospects for drug discovery. Pharmacol Res 2019; 150: 104520.
62. Fu J, Wang Z, Huang L, et al. Review of the botanical characteristics, phytochemistry, and pharmacology of Astragalus membranaceus (Huangqi): botany, phytochemistry, pharmacology of A. Membranaceus. Phytother Res 2014; 28: 1275-283.
63. Wang Y, Zhang L, Pan Y, et al. Investigation of invigorating Qi and activating blood circulation prescriptions in treating Qi deficiency and blood stasis syndrome of ischemic stroke patients: study protocol for a randomized controlled trial. Front Pharmacol 2020; 11: 892.
64. Yao W, Yang H, Ding G. Mechanisms of Qi-blood circulation and Qi deficiency syndrome in view of blood and interstitial fluid circulation. J Tradit Chin Med 2013; 33: 538-44.
65. Han JY, Li Q, Ma ZZ, Fan JY. Effects and mechanisms of compound Chinese medicine and major ingredients on microcirculatory dysfunction and organ injury induced by ischemia/reperfusion. Pharmacol Ther 2017; 177: 146-73.
66. Zhang H, Wan Z, Yan X, et al. Protective effect of Shenfu injection preconditioning on lung ischemia-reperfusion injury. Exp Ther Med 2016; 12: 1663-70.
67. Yu L, Li Q, Yu B, et al. Berberine attenuates myocardial ischemia/reperfusion injury by reducing oxidative stress and inflammation response: role of silent information regulator 1. Oxid Med Cell Longev 2016; 2016: 1-16.
68. Ma C, Zhang J, Yang S, et al. Astragalus flavone ameliorates atherosclerosis and hepatic steatosis via inhibiting lipid-disorder and inflammation in apoE-/- mice. Front Pharmacol 2020; 11: 610550.
69. Lin S, Huang L, Luo ZC, et al. The ATP level in the medial prefrontal cortex regulates depressive-like behavior via the medial prefrontal cortex-lateral habenula pathway. Biol Psychiatry 2022; 92: 179-92.
70. Cao X, Li LP, Wang Q, et al. Astrocyte-derived ATP modulates depressive-like behaviors. Nat Med 2013; 19: 773-77.
71. Wang XL, Feng ST, Wang YT, Chen NH, Wang ZZ, Zhang Y. Paeoniflorin: a neuroprotective monoterpenoid glycoside with promising anti-depressive properties. Phytomedicine 2021; 90: 153669.
72. Luan F, Ji Y, Peng L, et al. Extraction, purification, structural characteristics and biological properties of the polysaccharides from Codonopsis pilosula: a review. Carbohydr Polym 2021; 261: 117863.
73. Zuo C, Cao H, Song Y, et al. Nrf2: an all-rounder in depression. Redox Biol 2022; 58: 102522.
74. Park MK, Ji J, Haam K, et al. Licochalcone A inhibits hypoxia-inducible factor-1α accumulation by suppressing mitochondrial respiration in hypoxic cancer cells. Biomed Pharmacother 2021; 133: 111082.
75. Zhao Z, Wang W, Guo H, Zhou D. Antidepressant-like effect of liquiritin from Glycyrrhiza uralensis in chronic variable stress induced depression model rats. Behav Brain Res 2008; 194: 108-13.
Options
Outlines

/

〈 〉