an evidence-based approach to tcm patient class definition and differentiation nevin l. zhang the...

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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

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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.

Page 2

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

Page 8

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

Page 9

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.

Page 10

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.

Page 11

Outline

Introduction

Statistical validation of TCM postulates

Providing evidence for TCM patient class definition and

differentiation

Concluding remarks

Page 12

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.

Page 13

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

Page 14

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

Page 15

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

Page 16

Statistical Validation of TCM PostulatesPage 17

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

Page 21

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.

Page 22

Page 23

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

Page 24

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

Page 25

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

Page 26

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

Results on other Data Sets from a 973 Project

Page 27

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.

Page 28

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.

Page 29

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.

Page 30

Outline

Introduction

Statistical validation of TCM postulates

Providing evidence for TCM patient class definition and

differentiation

Concluding remarks

Page 31

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?

Page 32

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

Page 33

Cluster Analysis

Grouping of objects into clusters so that objects in the same cluster

are similar in some sense

Page 34

How to Cluster Those?Page 35

How to Cluster Those?Page 36

Style of picture

How to Cluster Those?Page 37

Type of object in picture

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

LTM for a Depression Data SetPage 40

Page 41

Partition given by Y15

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”

Page 42

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

Page 43

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.

Page 44

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

Page 45

Application of LTM in BioinformaticsPage 46

Thank You!