An Evidence-Based Approach to TCM Patient Class Definition and Differentiation

1. An Evidence-Based Approach to TCM Patient Class Definition and Differentiation Nevin L. Zhang The Hong Kong Univ. of Sci. & Tech. http://www.cse.ust.hk/~lzhang • Joint Work with： • HKUST: Yuan Shihong, Chen Tao, Wang Yi, Liu Tengfei, Poon Kin Man, Liu Hua • Beijing TCM U: Wang Tianfang, Zhao Yan, Xu Wenjie, Wang Qingguo • Shanghai TCM U: Xu Zhaoxia, Wang Yiqing Academy of TCM: Zhou Xuezhong, Zhang Runshun, Gong Yanbin, He Liyun, Wang Jie, Liu Baoyan • Beijing Dongfang Hospital: Zhang Yongling, Chen Boxing, Fu Chen

2. Traditional Chinese Medicine (TCM) is important to the Chinese people. • Culture tradition • Health care • It is used by many others. WHO report: • Global herbal medicine market: US$60 billion • Traditional medicine treatment at least once in life • 90% of Canadian, 49% of French people, • 48% of Australians, 42% of Americans. TCM is Worthy of Research

3. Spectrum of TCM Research

4. Spectrum of TCM Research

5. Spectrum of TCM Research

6. Spectrum of TCM Research

7. Spectrum of TCM Research

8. Western Medicine (Modern Biomedical Medicine ) • Human body: A machine with different parts, viewed at different levels: anatomic, biochemical, genetic • Disease: malfunction of some part • TCM • Human body: Dynamic system of energy and functions, holistic view • Disease: Disharmony • Among yin, yang, qi, xuĕ, zàng-fǔ, meridians etc. and/or • Between of the human body and the environment Western Medicine vs TCM: A Layman view

9. Both Modern Medicine and TCM divide patients into classes • Patient classes in modern medicine • Correspond to diseases at certain stages: E.g., Stage 4 COPD • Clearly defined • Have gold standard for differentiation • Patient classes in TCM • Correspond to pattern of disharmony (syndrome): Yang Deficiency • Not clearly defined • Differentiation heavilyinfluenced by subjectivity Patient Classes

10. Supervised learning • Labeled Data: Symptoms & signs, class labels assigned by experts • Provides quantitative summarization of experts’ know-hows • Conducive to the improvement of TCM service. Reduce variance. • However, it does not solve the subjectivity problem. • Our work: cluster analysis • Unlabeled Data: symptoms & signs only • Aim at finding natural clusters among patient population, which • Can be used as objective evidence for patient class definition. Data-Driven Research on Syndrome Differentiation

11. In clinic practice, syndrome differentiation is heavily influenced by objectivity. Our objective to provide evidence to make syndrome differentiation as objective as possible. Our Objective Status Quo Of Syndrome Differentiation Our Goal Ideal Case Subjective Reference Standard Objective Evidence + Subjective Judgment Objective Gold Standard

12. Introduction Statistical validation of TCM postulates Providing evidence for TCM patient class definition and differentiation Concluding remarks Outline

13. TCM has postulates to explain occurrence of symptoms: Kidney yang is the basis of all yang in the body. When kidney yang is in deficiency, it cannot warm the body and the patient feels cold, resulting in intolerance to cold, cold limbs, and cold lumbus and back. • Key question: • Do concepts such as “kidney yang deficiency” have scientific contents or are pure subjective notion? • Efforts to provide objective evidence would be in vain in the latter case. TCM Postulates

14. For more than 50 years, researchers have tried to show that • TCM syndrome factors correspond to real entities by means of biomedical laboratory tests, (recently genetic method also) • but there has been little success. • We take a data-analysis approach Research on Objectivity of TCM Syndrome

15. TCM postulate: Kidney yang is the basis of all yang in the body. When kidney yang is in deficiency, it cannot warm the body and the patient feels cold, resulting in intolerance to cold, cold limbs, and cold lumbus and back. • Manifest variables：Directly observed • Feel cold, cold limbs, intolerance to cold. • Latent variable: Not directly observed • Kidney Yang deficiency • Similar to concepts such as “intelligence” • Latent Structure • Relationships between latent variables and manifest variables TCM syndromes are latent variables

16. How did concepts such as “Intelligence” come into being? • Conjecture: From correlation between observed variables. • How do we possibly prove this? • LampPrinciple applet interactive demo … • Shows that human beings tend to introduce latent variables to explain co-occurrence in observations • Conjecture about TCM the formation postulates • Co-occurrence of cold symptoms => Kidney Yang deficiency Collective Cognition

17. Statistical Validation of TCM Postulates

18. Data Analysis Tool • Latent tree models: • Each node represents a discrete random variable • Arrows represent dependence • Leaves observed (manifest variables) • Internal nodes latent (latent variables) • Links quantify by probability distributions: P(Y1), P(Y2|Y1), P(X1|Y2), P(X2|Y2), …

19. Data Analysis Tool Learning latent tree models: Determine • Number of latent variables • Cardinality of each latent variable • Model Structure • Conditional probability distributions

20. Data Analysis Tool • How to learn latent tree models from data • Statistical Principle (BIC score) + Search

21. Page 21 Case Study • Kidney data • Population: Seniors aged 60 or above from residential communities • Variables: 34 symptoms associated with kidney deficiency • Sample size: 2600

22. Page 22 • Latent structure matches relevant TCM postulates • We have not shown “yang deficiency” corresponds to real entity • We have shown that the postulate of a “yang deficiency” entity would explain the co-occurrence patterns observed in data well.

23. Model Match between Model and TCM Postulates TCM Kidney yang deficiency, failing to warm body intolerance to cold, cold limbs, cold lumbus and back,  Spleen disorders  loose stools, indigested grain in the stool Good Match

24. Model Match between Model and TCM Postulates TCM When kidney fails to control the urinary bladder, • frequent urination, urine leakage after urination, frequent nocturnal urination, • (in severe cases) urinary incontinence and nocturnal enuresis. Good Match

25. Model Match between Model and TCM Postulates TCM kidney essence insufficiency premature baldness, tinnitus, deafness, poor memory, trance, declination of intelligence, fatigue, weakness, and so on. Good Match

26. Model Match between Model and TCM Postulates TCM kidney yin deficiency dry throat, tidal fever or hectic fever, fidgeting, hot sensation in the five centers,insomnia, yellow urine, rapid and thready pulse, and so on. Good Match

27. Page 27 Results on other Data Sets from a 973 Project

28. Page 28 Summary • We have analyzed many data sets • Latent variables obtained match the relevant TCM postulates in all cases • Conclusion: • TCM syndrome concepts do have scientific contents. • We have not shown that TCM syndromes corresponds to real entities. • We have shown that the postulate of the existence of such entities would explain the co-occurrence patterns observed in data.

29. D. Haughton and J. Haughton. Living Standards Analytics: Development through the Lens of Household Survey Data. Springer. 2012 • Zhang et al. provide a very interesting application of latent class models to diagnoses in traditional Chinese medicine (TCM). • The results tend to confirm known theories in Chinese traditional medicine. • This is a significant advance, since the scientific bases for these theories are not known. • The model proposed by the authors provides at least a statistical justification for them. Value of Work in View of Others

30. [Review of a recent paper]I am very interested in what these authors are trying to do. They are dealing with an important epistemological problem. To go from the many symptoms and signs that patients present, to construct a consistent and other-observer identifiable constellation, is a core task of the medical practitioner. A kind of feedback occurs between what a practitioner is taught/finds listed in books, and what that practitioner encounters in the clinic. The better the constellation is understood, the more accurate the clustering of symptoms, the more consistent is the identification of syndromes among practitioners and through time. While these constellations have been worked into widely-accepted ‘disease constructs’ for biomedicine for some time which are widely accepted as ‘real,’ this is not quite as true for TCM constellations. This latent variable study is interesting not only in itself, but also as providing evidence that what TCM ‘says’ is so, shows up during analysis as demonstrably so. Value of Work in View of Others

31. Introduction Statistical validation of TCM postulates Providing evidence for TCM patient class definition and differentiation Concluding remarks Outline

32. Common practice in China • Patients of a WM disease subdivided into several TCM classes • Example: • WM disease: Depression • TCM Classes: • Liver-Qi Stagnation (肝气郁结), Stagnation of liver qi and spleen deficiency (肝郁脾虚), Deficiency of both heart and spleen (心脾两虚), Liver depression forming fire (肝郁化火), …. • No agreed sub-classing standard • 5 different standards proposed by different organizations/groups • Based experts’ opinions • Can we provide evidence for the TCM sub-typing of WM diseases? Integration of TCM and Western Medicine

33. Page 33 The Idea • Imagine sub-typing Western medicine disease D from TCM perspective • Also providing a basis for defining syndrome Z and for differentiating syndrome Z patients from other D patients

34. Grouping of objects into clusters so that objects in the same cluster are similar in some sense Cluster Analysis

35. How to Cluster Those? Page 35

36. How to Cluster Those? Page 36 Style of picture

37. How to Cluster Those? Page 37 Type of object in picture

38. How to Cluster Those? Page 38 Multidimensional clustering / Multi-Clustering • How to partition data in multiple ways? • Latent tree models

39. Latent Tree Models & Multidimensional Clustering • Model relationship between • Observed / Manifest variables • Math Grade, Science Grade, Literature Grade, History Grade • Latent variables • Analytic Skill, Literal Skill, Intelligence • Each latent variable gives a partition • Intelligence: Low, medium, high • Analytic skill: Low, medium, high

40. LTM for a Depression Data Set

41. Partition given by Y15

42. We now have the empirical partition. What is the Z? In TCM, the symptoms “shortness of breath” etc. characterize Qi movement disorder in chest (胸膈气机不畅). So, Z should be “whether Qi movement disorder in chest” What is the Z?

43. Previously, no clear definition for the class • Qi movement disorder in chest (胸膈气机不畅). • Empirical partition gives us a clear definition • s1: Qi movement disorder in chest (胸膈气机不畅), • s0: no Qi movement disorder in chest (无胸膈气机不畅) • Sizes of the classes: 48%，52%； • Class differentiation: Bayes rule, importance of symptoms indicated by ratios Patient Class Definition and Differentiation

44. For clinic convenience, differentiation standards are usually given by a scoring system: • Current work: • Derive such scoring systems from results of latent tree analysis, particularly the probability ratios. Easy-to-Operate Differentiation Standards

45. Concluding Remarks • Latent tree analysis is tool for • Systematically identifying co-occurrence patterns of symptoms • Introduce latent structure to explain the patterns • Provide evidence in support of TCM postulates about symptom occurrence • Tool for multidimensional clustering • Each latent variable represents a partition of data • Provide evidence for TCM patient class definition and differentiation

46. Application of LTM in Bioinformatics

48. Thank You！