Latent Tree Models Part IV: Applications

Latent Tree ModelsPart IV: Applications

Nevin L. ZhangDept. of Computer Science & Engineering

The Hong Kong Univ. of Sci. & Tech.http://www.cse.ust.hk/~lzhang

AAAI 2014 Tutorial

AAAI 2014 Tutorial Nevin L. Zhang HKUST

2

What can LTA be used for: Discovery of co-occurrence patterns in binary data Discovery of correlation patterns in general discrete data Discovery of latent variable/structures Multidimensional clustering Topic detection in text data Probabilistic modelling

Applications Analysis of survey data

Market survey data, social survey, medical survey data Analysis of text data

Topic detection Approximate probabilistic inference

Applications of Latent Tree Analysis (LTA)


3

Part IV: Applications

Approximate Inference in Bayesian Networks

Analysis of social survey data

Topic detection in text data

Analysis of medical symptom survey data

Software


4

Attractive Representation of Joint Distributions Computationally very simple to work with. Represent complex relationships among observed

variables. What does the structure look like without the latent variables?

LTMs for Probabilistic Modelling


5

In a Bayesian network over observed variables, exact inference can be computationally prohibitive.

Two-phase approximate inference: Offline

Sample data set from the original network Learn a latent tree model (secondary representation)

Online Make inference using the latent tree model. (Fast)

Approximate Inference in Bayesian Networks

(Wang et al. AAAI 2008)

Sample Learn LTM


6

Alternatives LTM (1k), LTM (10k), LTM (100k): with different sample size for

Phase 1. CL (100k): Phase 1 learns Chow-Liu tree LCM (100k): Phase 1 learns latent class model Loopy Belief Propagation (LBP)

Original networks ALARM, INSURANCE, MILDEW, BARLEY, etc.

Evaluation: 500 random queries Quality of approximation measured using KL from exact answer.

Empirical Evaluations


7

C: cardinality of latent variables

When C is large enough, LTM achieves good approximation in all cases.

Better than LBP on g, d,h

Better than CL on d, h.

Key Advantage: Online phase is 2 to 3 orders of magnitude faster than exact inference

Empirical Resultssparse dense


8

Part III: Applications

Approximate Inference in Bayesian networks


Topic detection


Software


9

Social Survey Data// Survey on corruption in Hong Kong and performance of the anti-corruption

agency -- ICAC

//31 questions, 1200 samplesC_City: s0 s1 s2 s3 // very common, quite common, uncommon, very uncommonC_Gov: s0 s1 s2 s3 C_Bus: s0 s1 s2 s3 Tolerance_C_Gov: s0 s1 s2 s3 //totally intolerable, intolerable, tolerable, totally tolerableTolerance_C_Bus: s0 s1 s2 s3WillingReport_C: s0 s1 s2 // yes, no, dependsLeaveContactInfo: s0 s1 // yes, noI_EncourageReport: s0 s1 s2 s3 s4 // very sufficient, sufficient, average, ...I_Effectiveness: s0 s1 s2 s3 s4 //very e, e, a, in-e, very in-eI_Deterrence: s0 s1 s2 s3 s4 // very sufficient, sufficient, average, ...…..-1 -1 -1 0 0 -1 -1 -1 -1 -1 -1 0 -1 -1 -1 0 1 1 -1 -1 2 0 2 2 1 3 1 1 4 1 0 1.0-1 -1 -1 0 0 -1 -1 1 1 -1 -1 0 0 -1 1 -1 1 3 2 2 0 0 0 2 1 2 0 0 2 1 0 1.0-1 -1 -1 0 0 -1 -1 2 1 2 0 0 0 2 -1 -1 1 1 1 0 2 0 1 2 -1 2 0 1 2 1 0 1.0….


10

Latent Structure Discovery

Y2: Demographic info; Y3: Tolerance toward corruption;Y4: ICAC performance; Y5: Change in level of corruption; Y6: Level of corruption; Y7: ICAC accountability


11

Multidimensional Clustering

Y2=s0: Low income youngsters; Y2=s1: Women with no/low income;Y2=s2: people with good education and good income; Y2=s3: people with poor education and average income.


12

Multidimensional Clustering

Y3=s0: people who find corruption totally intolerable; 57%Y3=s1: people who find corruption intolerable; 27%Y3=s2: people who find corruption tolerable; 15%

Interesting finding: Y3=s2: 29+19=48% find C-Gov totally intolerable or intolerable; 5% for C-BusY3=s1: 54% find C-Gov totally intolerable; 2% for C-BusY3=s0: Same attitude toward C-Gov and C-Bus

People who are tough on corruption are equally tough toward C-Gov and C-Bus.People who are lenient about corruption are more lenient C-Bus than C-GOv


13

Multidimensional Clustering Who are the toughest toward corruption among the 4 groups?

Y2=s2: ( good education and good income) the least tolerant. 4% tolerableY2=s3: (poor education and average income) the most tolerant. 32% tolerableThe other two classes are in between.

Summary: Latent tree analysis of social survey data can reveal• Interesting latent structures • Interesting clusters• Interesting relationships among the clusters.


14


Approximate Inference


Topic detection (Analysis of text data)


Software


15

Basics

Aggregation of miniature topics

Topic extraction and characterization

Empirical results

Latent Tree Models for Topic Detection

Topic: State of latent variable, soft collection of documents Characterized by: Conditional probability of word given latent state, or, document

frequency of word in collection: # docs containing the word / total # of docs in the topic

Probabilities all words for a topic (in a column) do not sum to 1.

Y1=2: oop; Y1=1: Programming; Y1=0: background Background topics for other latent variables not shown.

What is a topic in LTA?LTM for

toy text data


17

Topic: A collection of documents A document is a member of a topic

Can belong to multiple topics with different probabilities Probabilities for each document (in each row) do not sum to 1.

How are topics and documents are related?

D97, D115, D205, D528 are documents from the toy text dataTable shows: D97 is a web page on OOP from U of Wisconsin Madison D528 is a web page on AI from U of Texas Austin

LDA Topic: Distribution over vocabulary Frequencies a writer would use each word when writing about the

topic Probabilities for a topic (in a column) sum to 1

In LDA a document is a mixture of topics (LTA: Topic is a collection of documents) Probabilities in each row sum to 1

LTA Differs from Latent Dirichlet Allocation (LDA)


19

Basics



Empirical results



20

Latent variable give miniature topics.

Intuitively, more interesting topics can be detected if we combine Z11, Z12, Z13 Z14, Z15, Z16 Z17, Z18, Z19

BI algorithm produces flat models: Each latent variable directly connected to at least one observed variables.

Latent Tree Model for a Subset of Newsgroup Data


21

Convert the latent variables into observed one via hard assignment. Afterwards, Z11-Z19 become observed.

Run BI on Z11-Z19

Hierarchical Latent Tree Analysis (HLTA)


22

Stack model for Z11-Z19 on top of model for the words

Repeat until no more than 2 latent variables or predetermined level reached.

The result is called a hierarchical latent tree model (HLTM)



23

Part II: Cannot determine edge orientations based solely on data.

Here hierarchical structure introduced to improve model interpretability.

Data + interpretability hierarchical structure.

It does not necessarily improve model fit.



24

Basics



Empirical results



25

Interpreting states of Z21 Z11, Z12, and Z13 introduced because of co-occurrence of

“computer”, “Science”; “card”, “display”, …., “video”; and “dos” , “windows”

Z21 introduced because of correlations among Z11, Z12, Z13

So, interpretation of the states of Z21 is to be based on the words in the sub-tree rooted at Z21. They form the semantic base of Z21.

Semantic Base


26

Semantic base might be too large to handle. Effective base: Subset of semantic base that matters.

Sort variables Xi from semantic base in descending of I(Z; Xi). I(Z; X1, …, Xi): Mutual information between Z and first i-th variables

Estimated via sampling, increases with i. I(Z; X1, …, Xm): Mutual information between Z and all m variables in

semantic base

Information coverage of the first i-th variable I(Z; X1, …, Xi)/ I(Z; X1, …, Xm):

Effective semantic base: Set of leading variables with information coverage higher than a certain

level, i.e., 95%.

Effective Semantic BaseChen et al. AIJ

2012

Effective semantic bases are typically smaller than Semantic bases. Z22: Semantic base --10 variables, Effective semantic base – 8

variable Differences are much larger in models with hundreds of

variables. Words are the front are more informative in distinguishing

between the states of the latent variable.

Z22:

Upper: Information coverageLower: Mutual Information


28

HLTA characterizes Latent state (topics) using probabilities of words from effective semantic base NOT sorted according to probability, but mutual

information Topic Z22=s1 characterized using words

Occur with high probabilities in documents on to the topic, and

Occur with low probability in documents NOT on the topic.

LDA, HLDA, … Topic characterized using words that occur with highest

probability in the topic. Not necessarily the best words to distinguish the topic

from other topics.

Topic Characterizations


29

Basics



Empirical results



30

Show the results of HLTA on real-world data

Compare HLTA with HLDA and LDA

Empirical Results


31

1,740 papers published at NIPS between 1988 – 1999. Vocabulary:

1,000 words selected using average TF-IDF.

HLTA produced a model with 382 latent variables, arranged on 5 levels. Level 1 – 279; Level 2 – 72; Level 3 - 21; Level 4 - 8; Level 5 - 2

Example topics on next few slides Topic characterizations, topic sizes, Topic groups, topic group labels.

For details: http://www.cse.ust.hk/~lzhang/ltm/index.htm

NIPS Data

likelihood bayesian statistical gaussian conditional 0.34 likelihood bayesian statistical conditional 0.16 gaussian covariance variance matrix 0.21 eigenvalues matrix gaussian covariance

trained classification classifier regression classifiers 0.25 validation regression svm machines 0.07 svm machines vapnik regression 0.38 trained test table train testing 0.30 classification classifier classifiers class cl

images image pixel pixels object 0.25 images image pixel pixels texture 0.16 receptive orientation objects object 0.21 object objects perception receptive

hidden propagation layer backpropagation units 0.40 hidden backpropagation multilayer architecture architectures 0.40 propagation layer units back net

HLTA Topics: Level-3 reinforcement markov speech

hmm transition 0.20 markov speech speaker hmms hmm 0.14 speech hmm speaker hmms markov 0.13 reinforcement sutton barto policy actions 0.10 reinforcement sutton barto actions policy

cells neurons cortex firing visual 0.17 visual cells cortical cortex activity 0.27 cells cortex cortical activity visual 0.33 neurons neuron synaptic synapses 0.18 membrane potentials spike spikes firing 0.15 firing spike membrane spikes potentials 0.18 circuit voltage circuits vlsi chip 0.26 dynamics dynamical attractor stable attractors

…..

markov speech hmm speaker hmms 0.14 markov stochastic hmms sequence hmm 0.10 hmm hmms sequence markov stochastic 0.15 speech language word speaker acoustic 0.06 speech speaker acoustic word language 0.16 delay cycle oscillator frame sound 0.10 frame sound delay oscillator cycle 0.14 strings string length symbol

HLTA Topics: Level-2 reinforcement sutton barto

actions policy 0.12 transition states reinforcement reward 0.10 reinforcement policy reward states 0.14 trajectory trajectories path adaptive 0.12 actions action control controller agent 0.09 sutton barto td critic moore

likelihood bayesian statistical conditional posterior

0.34 likelihood statistical conditional density

0.35 entropy variables divergence mutual 0.19 probabilistic bayesian prior posterior 0.11 bayesian posterior prior bayes 0.15 mixture mixtures experts latent 0.14 mixture mixtures experts hierarchical 0.34 estimate estimation estimating estimated 0.21 estimate estimation estimates estimated

gaussian covariance matrix variance eigenvalues 0.09 matrix pca gaussian covariance variance 0.23 gaussian covariance variance matrix pca 0.09 pca gaussian matrix covariance variance 0.18 eigenvalues eigenvalue eigenvectors ij 0.15 blind mixing ica coefficients inverse

HLTA Topics: Level-2 regression validation vapnik svm

machines 0.24 regression svm vapnik margin kernel 0.05 svm vapnik margin kernel regression 0.19 validation cross stopping pruning 0.07 machines boosting machine boltzmann

classification classifier classifiers class classes 0.28 classification classifier classifiers class 0.24 discriminant label labels discrimination 0.13 handwritten digit character digits

trained test table train testing 0.38 trained test table train testing 0.44 experiments correct improved improvement correctly

…

likelihood statistical conditional density log 0.30 likelihood conditional log em maximum 0.42 statistical statistics 0.19 density densities

entropy variables variable divergence mutual 0.16 entropy divergence mutual 0.31 variables variable

bayesian posterior probabilistic prior bayes 0.19 bayesian prior bayes posterior priors 0.09 bayesian posterior prior priors bayes 0.29 probabilistic distributions probabilities 0.16 inference gibbs sampling generative 0.19 mackay independent averaging ensemble 0.08 belief graphical variational 0.09 monte carlo 0.09 uk ac

HLTA Topics: Level-1 mixture mixtures experts

hierarchical latent 0.19 mixture mixtures 0.34 multiple individual missing hierarchical 0.15 hierarchical sparse missing multiple 0.07 experts expert 0.32 weighted sum

estimate estimation estimated estimates estimating 0.38 estimate estimation estimated estimating 0.19 estimate estimates estimation estimated 0.29 estimator true unknown 0.33 sample samples 0.40 assumption assume assumptions assumed 0.27 observations observation observed

…

Reason for aggregate miniature topics: Many Level 1 topics correspond to trivial word co-occurrences , not meaningful

Level 5 visual cortex cells neurons

firing 0.37 visual cortex firing neurons cells 0.39 visual cells firing cortex neurons 0.25 images image pixel hidden trained 0.09 hidden trained images image pixel 0.20 trained hidden images image pixel 0.15 image images pixel trained hidden

HLTA Topics: Level-4 & 5 Level 4 visual cortex cells neurons firing

0.34 cells cortex firing neurons visual 0.28 cells neurons cortex firing visual 0.41 approximation gradient optimization 0.29 algorithms optimal approximation 0.39 likelihood bayesian statistical gaussian

images image trained hidden pixel 0.22 regression classification classifier 0.29 trained classification classifier classifiers 0.02 classification classifier regression 0.28 learn learned structure feature features 0.23 feature features structure learn learned 0.24 images image pixel pixels object 0.13 reinforcement transition markov speech 0.14 speech hmm markov transition 0.40 hidden propagation layer backpropagation units


37

Level 1: 279 latent variables Many capture trivial word co-occurrence patterns

Level 2: 72 latent variables Meaningful topics, and meaningful topic groups

Level 3 : 21 latent variables Meaningful topics, and meaningful topic groups More general than Level 2 topics

Level 4: 8 latent variables Meaningful topics, very general

Level 5: 2 latent variables Too few

In application, one can choose to output the topics at a certain level according the desired number of topics. For NIPS data, either level-2 topics or level-3 topics.

Summary of HLTA Results on NIPS Data

units hidden layer unit weight gaussian log density likelihood estimate margin kernel support xi bound generalization student weight teacher optimal gaussian bayesian kernel evidence posterior chip analog circuit neuron voltage classifier rbf class classifiers classification speech recognition hmm context word ica independent separation source sources image images matching level object tree trees node nodes boosting variables variable bayesian conditional family face strategy differential functional weighting source grammar sequences polynomial regression derivative em machine annealing max min

regression prediction selection criterion query

validation obs generalization cross pruning mlp risk classifier classification confidence loss song transfer bounds wt principal curve eq curves rules

HLDA Topics control optimal algorithms

approximation step policy action reinforcement states actions experts mixture em expert gaussian convergence gradient batch descent means control controller nonlinear series forward distance tangent vectors euclidean distances robot reinforcement position control path bias variance regression learner exploration blocks block length basic experiment td evaluation features temporal expert path reward light stimuli paths Long hmms recurrent matrix term channel call cell channels rl

image images recognition pixel feature video motion visual speech recognition face images faces recognition facial ocular dominance orientation cortical cortex character characters pca coding field resolution false true detection context

….


39

inputs outputs trained produce actualdynamics dynamical stable attractor synaptic synapses inhibitory excitatory correlation power correlations cross states stochastic transition dynamic basis rbf radial gaussian centerssolution constraints solutions constraint type elements group groups elementedge light intensity edges contourrecurrent language string symbol stringspropagation back rumelhart bp hintonii region regions iii chaingraph matching annealing match context mlp letter nn lettersfig eq proposed fast procvariables variable belief conditional ipp vol ca eds ieee

LDA Topicsunits unit hidden connections connectedhmm markov probabilities hidden hybridobject objects recognition view shaperobot environment goal grid worldentropy natural statistical log statisticsexperts expert gating architecture jordantrajectory arm inverse trajectories handsequence step sequences length sgaussian density covariance densities positive negative instance instances nptarget detection targets FALSE normalactivity active module modules brainmixture likelihood em log maximumchannel stage channels call routingterm long scale factor range…


40

HLTA Topics likelihood bayesian statistical

conditional posterior 0.34 likelihood statistical conditional density

0.35 entropy variables divergence mutual 0.19 probabilistic bayesian prior posterior 0.11 bayesian posterior prior bayes 0.15 mixture mixtures experts latent 0.14 mixture mixtures experts hierarchical

reinforcement sutton barto actions policy 0.12 transition states reinforcement reward 0.10 reinforcement policy reward states 0.14 trajectory trajectories path adaptive 0.12 actions action control controller agent 0.09 sutton barto td critic moore

Comparisons between HLTA and HLDAHLDA Topics gaussian log density likelihood estimate margin kernel support xi bound generalization student weight teacher optimal gaussian bayesian kernel evidence posterior chip analog circuit neuron voltage classifier rbf class classifiers classification speech recognition hmm context word

control optimal algorithms approximation step policy action reinforcement states actions experts mixture em expert gaussian convergence gradient batch descent means control controller nonlinear series forward distance tangent vectors euclidean distances robot reinforcement position control path bias variance regression learner exploration blocks block length basic experiment

HLTA topics have sizes, HLDA/LDA topics do not HLTA produces better hierarchy HLTA gives better topic characterizations


41

Suppose a topic t is described using M words The topic coherence score for t is:

Idea The words for a topic would tend to co-occur. Given a list of words, the more often the words co-occur, than the better

the list is as a definition of a topic. Note:

Score decreases with M. Topics be compared should be described using the same number of

words

Measure of Topic Quality

D. Mimno, H. M. Wallach, E. Talley, M. Leenders, and A. McCallum. Optimizing semantic coherence in topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 262–272, 2011.


42

HLTA (L3-L4): All non-background topics from Levels 3 and 4: 47 HLTA (L2-L3-L4): All non-background topics from Levels 2, 3 and 4: 140

LDA was instructed to find two sets of topics with 47 and140 topics

HLDA found more 179. HLDA-s: A subset of the HLDA topics were sampled for fair comparison.

HLTA Found More Coherent Topics than LDA and HLDA


43

Regard LDA, HLDA and HLTA as methods for text modeling Build a probabilistic model for the corpus

Evaluation: Per-document held-out loglikelihood (-log(perplexity)). Measure performance of model on predicting unseen data

Data: NIPS: 1,740 papers from NIPS, 1,000 words, JACM: 536 abstracts from J of ACM, 1,809 words. NEWSGROUP: 20,000 newsgroup posts, 1,000 words.

Comparisons in Terms of Model Fit

HLTA results robust w.r.t UD-test threshold The values 1, 3, 5 are from literature on Bayes factor (see Part III)

LDA produced by far worst models in all cases. HLTA out-performed HLDA on NIPS, tied on JACP, and beaten on

Newsgroup Caution: Better model does not implies better topics

Running time on NIPS: LDA – 3.6 hours, HLTA – 17 hours, HLDA – 68 hours.


45

HLTA Topic: collection of documents Have sizes Characterization: Words occur

with high probability in topic, low probability in other documents

Document: A member of topic, can belong to multiple topics with probability 1.

Summary LDA, HLDA

Topic: Distribution over vocabulary

Don’t have sizes Characterization: Words occur

with high probability in topic

Document: A mixture of topics

HLTA produces better hierarchy than HLDA HLTA produce more coherent topics than LDA and HLDA


46




Topic detection


Software


47

Background of Research

Common practice in China, increasingly in Western world Patients of a WM disease divided into several TCM classes Different classes are treated differently using TCM treatments.

Example: WM disease: Depression TCM Classes:

Liver-Qi Stagnation ( 肝气郁结 ). Treatment principle: 疏肝解郁， Prescription: 柴胡疏肝散

Deficiency of Liver Yin and Kidney Yin ( 肝肾阴虚 ) ： Treatment principle: 滋肾养肝， Prescription: 逍遥散合六味地黄丸

Vacuity of both heart and spleen ( 心脾两虚 ). Treatment principle: 益气健脾 , Prescription: 归脾汤 ….

Page 47


48

Key Question How should patients of a WM disease be divided into

subclasses from the TCM perspective? What TCM classes? What are the characteristics of each TCM class? How to differentiate different TCM classes?

Important for Clinic practice Research

Randomized controlled trials for efficacy Modern biomedical understanding of TCM concepts

No consensus. Different doctors/researchers use different schemes. Key weakness of TCM.

Page 48


49

Key Idea Our objective:

Provide an evidence-based method for TCM patient classification Key Idea

Cluster analysis of symptom data => empirical partition of patients

Check to see whether it corresponds to TCM class concept

Key technology: Multidimensional clustering Motivation for developing latent tree analysis

Page 49


50

Symptoms Data of Depressive Patients

Subjects: 604 depressive patients aged between 19 and 69 from 9

hospitals Selected using the Chinese classification of mental disorder

clinic guideline CCMD-3 Exclusion:

Subjects we took anti-depression drugs within two weeks prior to the survey; women in the gestational and suckling periods, .. etc

Symptom variables From the TCM literature on depression between 1994 and 2004. Searched with the phrase “ 抑郁 and 证” on the CNKI (China

National Knowledge Infrastructure) data Kept only those on studies where patients were selected using

the ICD-9, ICD-10, CCMD-2, or CCMD-3 guidelines. 143 symptoms reported in those studies altogether.

Page 50

(Zhao et al. JACM 2014)


51

The Depression Data Data as a table

604 rows, each for a patient 143 columns, each for a symptom Table cells: 0 – symptom not present, 1 – symptom present

Removed: Symptoms occurring <10 times 86 symptoms variables entered latent tree analysis.

Structure of the latent tree model obtained on the next two slides.

Page 51


52

Model Obtained for a Depression Data (Top)

Page 52


53

Model obtained for a Depression Data (Bottom)

Page 53


54

The Empirical Partitions

Page 54

The first cluster (Y29= s0) consists of 54% of the patients and while the cluster (Y29= s1) consists of 46% of the patients.

The two symptoms ‘fear of cold’ and ‘cold limbs’ do not occur often in the first cluster

While they both tend to occur with high probabilities (0.8 and 0.85) in the second cluster.


55

Probabilistic Symptom co-occurrence pattern Probabilistic symptom co-occurrence pattern:

The table indicates that the two symptoms ‘fear of cold’ and ‘cold limbs’ tend to co-occur in the cluster Y29= s1

Pattern meaningful from the TCM perspective. TCM asserts that YANG DEFICIENCY ( 阳虚 ) can lead to, among other

symptoms, ‘fear of cold’ and ‘cold limbs’ So, the co-occurrence pattern suggests the TCM symdrome type

（证型） YANG DEFICIENCY ( 阳虚 ).

Page 55

The partition Y29 suggests that Among depressive patients, there is a

subclass of patient with YANG DEFICIENCY. In this subclass, ‘fear of cold’ and ‘cold

limbs’ co-occur with high probabilities (0.8 and 0.85)


56

Probabilistic Symptom co-occurrence pattern

Page 56

Y28= s1 captures the probabilistic co-occurrence of ‘aching lumbus’, ‘lumbar pain like pressure’ and ‘lumbar pain like warmth’.

This pattern is present in 27% of the patients. It suggests that

Among depressive patients, there is a subclass that correspond to the TCM concept of KIDNEY DEPRIVED OF NOURISHMENT ( 肾虚失养 )

Characteristics of the subclass given by distributions for Y28= s1


57


Y27= s1 captures the probabilistic co-occurrence of ‘weak lumbus and knees’ and ‘cumbersome limbs’.

This pattern is present in 44% of the patients It suggests that,

Among depressive patients, there is a subclass that correspond to the TCM concept of KIDNEY DEFICIENCY （肾虚）

Characteristics of the subclass given by distributions for Y27= s1 Y27, Y28, Y29 together provide evidence for defining KIDNEY YANG DEFICIENCY


58


Pattern Y21= s1: evidence for defining STAGNANT QI TURNING INTO FIRE （气郁化火）

Y15= s1 : evidence for defining QI DEFICIENCY Y17 = s1 : evidence for defining HEART QI DEFICIENCY Y16= s1 : evidence for defining QI STAGNATION Y19= s1: evidence for defining QI STAGNATION IN HEAD

Page 58


59


Y9= s1 :evidence for defining DEFICIENCY OF BOTH QI AND YIN ( 气阴两虚 ) Y10= s1: evidence for defining YIN DEFICIENCY ( 阴虚 ) Y11= s1: evidence for defining DEFICIENCY OF STOMACH/SPLEEN YIN

( 脾胃阴虚 )

Page 59


60

Symptom Mutual-Exclusion Patterns Some empirical partitions reveal

symptom exclusion patterns

Y1 reveals the mutual exclusion of ‘white tongue coating’, ‘yellow tongue coating’ and ‘yellow-white tongue coating’

Y2 reveals the mutual exclusion of ‘thin tongue coating’, ‘thick tongue coating’ and ‘little tongue coating’.

Page 60


61

Summary of TCM Data Analysis By analyzing 604 cases of depressive patient data using latent tree

models we have discovered a host of probabilistic symptom co-occurrence patterns and symptom mutual-exclusion patterns.

Most of the co-occurrence patterns have clear TCM syndrome connotations, while the mutual-exclusion patterns are also reasonable and meaningful.

The patterns can be used as evidence for the task of defining TCM classes in the context of depressive patients and for differentiating between those classes.

Page 61


62

Another Perspective: Statistical Validation of TCM Postulates

Page 62

(Zhang et al. JACM 2008)

Yang Deficiency Y29 = s1

Kidney deprived of nourishment

Y28 = s1

TCM terms such as Yang Deficiency were introduced to explain symptom co-occurrence patterns observed in clinic practice.

….. …..


63

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 (tree) 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 63


64




Topic detection


Software


65

http://www.cse.ust.hk/faculty/lzhang/ltm/index.htm Implementation of LTM learning algorithms: EAST, BI Tool for manipulate LTMs: Lantern LTM for topic detection: HLTA

Implementation of other LTM learning algorithms BIN-A, BIN-G, CL and LCM: http://people.kyb.tuebingen.mpg.de/harmeling/code/ltt-1.4.tar CFHLC: https://sites.google.com/site/raphaelmouradeng/home/programs NJ, RG, CLRG and regCLRG: http://people.csail.mit.edu/myungjin/latentTree.html − NJ (fast implementation): http://nimbletwist.com/software/ninja

Software

http://www.cse.ust.hk/faculty/lzhang/ltm/index.htm

http://www.cse.ust.hk/faculty/lzhang/ltm/index.htm

https://sites.google.com/site/raphaelmouradeng/home/programs



Documents

Latent Tree Models Part IV: Applications