An Evidence-Based Approach toTCM Patient Class Definition and Differentiation
Nevin L. ZhangThe 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 QingguoShanghai TCM U: Xu Zhaoxia, Wang YiqingAcademy of TCM: Zhou Xuezhong, Zhang Runshun, Gong Yanbin, He Liyun, Wang Jie, Liu BaoyanBeijing Dongfang Hospital: Zhang Yongling, Chen Boxing, Fu Chen
TCM is Worthy of Research
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.
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Spectrum of TCM ResearchPage 3
A visit to TCM Doctor
Patient Information Collection:•Inspection ( 望 ))•Auscultation & Olfaction ( 闻 ))•Inquiry ( 问 ))•Palpation ( 切 ))
Patient Classification:•Syndrome differentiation (辨证 )•Determine pattern of disharmony
Treatment:•Herbal medicine•Acupuncture•Tui Na, Cupping, Qigong, .., etc
Spectrum of TCM ResearchPage 4
A visit to TCM Doctor Research
Patient Information Collection:•Inspection ( 望 ))•Auscultation & Olfaction ( 闻 ))•Inquiry ( 问 ))•Palpation ( 切 ))
Instruments:
…….
Patient Classification:•Syndrome differentiation (辨证 )•Determine pattern of disharmony
Treatment:•Herbal medicine•Acupuncture•Tui Na, Cupping, Qigong, .., etc
Spectrum of TCM ResearchPage 5
A visit to TCM Doctor Research
Patient Information Collection:•Inspection ( 望 ))•Auscultation & Olfaction ( 闻 ))•Inquiry ( 问 ))•Palpation ( 切 ))
Instruments:
…….
Patient Classification:•Syndrome differentiation (辨证 )•Determine pattern of disharmony
Treatment:•Herbal medicine•Acupuncture•Tui Na, Cupping, Qigong, .., etc
• Efficacy• Effective component of herbs• Action mechanism • Safety• ….
Spectrum of TCM ResearchPage 6
A visit to TCM Doctor Research
Patient Information Collection:•Inspection ( 望 ))•Auscultation & Olfaction ( 闻 ))•Inquiry ( 问 ))•Palpation ( 切 ))
Instruments:
…….
Patient Classification:•Syndrome differentiation (辨证 )•Determine pattern of disharmony
• Supervised learning• Labeled Data: Symptoms & signs,
class labels assigned by expert
Treatment:•Herbal medicine•Acupuncture•Tui Na, Cupping, Qigong, .., etc
• Efficacy• Effective component of herbs• Action mechanism • Safety• ….
Spectrum of TCM ResearchPage 7
A visit to TCM Doctor Research
Patient Information Collection:•Inspection ( 望 ))•Auscultation & Olfaction ( 闻 ))•Inquiry ( 问 ))•Palpation ( 切 ))
Instruments:
…….
Patient Classification:•Syndrome differentiation (辨证 )•Determine pattern of disharmony
• Supervised learning• Labeled Data: Symptoms & signs,
class labels assigned by expert• Our work: cluster analysis
• Unlabeled Data: symptoms & signs
Treatment:•Herbal medicine•Acupuncture•Tui Na, Cupping, Qigong, .., etc
• Efficacy• Effective component of herbs• Action mechanism • Safety• ….
Western Medicine vs TCM: A Layman view
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
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Patient Classes
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 heavily influenced by subjectivity
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Data-Driven Research on Syndrome Differentiation
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.
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Our Objective
In clinic practice, syndrome differentiation is heavily influenced by
objectivity.
Our objective to provide evidence to make syndrome differentiation as
objective as possible.
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Outline
Introduction
Statistical validation of TCM postulates
Providing evidence for TCM patient class definition and
differentiation
Concluding remarks
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TCM Postulates
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.
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Research on Objectivity of TCM Syndrome
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
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TCM syndromes are latent variables
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
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Collective Cognition
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
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Data Analysis ToolPage 18
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), …
Data Analysis Tool
Learning latent tree models: Determine• Number of latent variables• Cardinality of each latent variable• Model Structure• Conditional probability distributions
Data Analysis ToolPage 20
How to learn latent tree models from data
Statistical Principle (BIC score) + Search
Case Study
Kidney data Population: Seniors aged 60 or above from residential
communities
Variables: 34 symptoms associated with kidney deficiency
Sample size: 2600
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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.
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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
Model
Good Match
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Match between Model and TCM Postulates
TCMWhen kidney fails to control the urinary
bladder,
frequent urination, urine leakage after
urination, frequent nocturnal urination,
(in severe cases) urinary incontinence
and nocturnal enuresis.
Model
Good Match
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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.
Model
Good Match
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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.
Model
Good Match
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.
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Value of Work in View of Others
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.
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Value of Work in View of Others
[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.
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Outline
Introduction
Statistical validation of TCM postulates
Providing evidence for TCM patient class definition and
differentiation
Concluding remarks
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Integration of TCM and Western Medicine
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?
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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
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Cluster Analysis
Grouping of objects into clusters so that objects in the same cluster
are similar in some sense
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How to Cluster Those?Page 38
Multidimensional clustering / Multi-Clustering
How to partition data in multiple ways? Latent tree models
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
What is the Z?
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”
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Patient Class Definition and Differentiation
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
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Easy-to-Operate Differentiation Standards
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.
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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
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