MPhil Thesis Defence


"3-level Latent Structure Models for TCM Data Analysis"

By

Mr. Dong Hua


Abstract

At the heart of traditional Chinese medicine (TCM) is the concept of
syndrome type, which refers to the pathological condition of a patient at
a given stage of a disease. Unlike diagnosis in Western medicine,
diagnosis in TCM means, in most cases, to determine a patient's syndrome
type instead of his disease type. A patient's syndrome type is not
directly observed. Rather, it is indirectly assessed on the basis of
symptoms. A major weakness of TCM is that syndrome types are not
rigorously defined, leading to subjectiveness and arbitrariness in
diagnosis. To rectify the situation, researchers in China have been
seeking for gold standards for TCM diagnosis for forty years.
Unfortunately, no such standards have been found.

Latent class analysis (LCA) is a popular method for model-based
clustering. It has been used in western medicine, psychology, and social
science to provide statistical justification and qualification for latent
concepts. In this thesis, we first apply LCA to two TCM data sets. We find
that the latent class (LC) models resulting from LCA fit data poorly. We
hence study a generalization of LC models called 3-level latent structure
(3-LS) models. With the generalization, we are able to find models that
fit the data well. Those models might provide TCM researchers insights
about TCM theories and be used to guide diagnosis in practice.


Date:			Thursday, 25 July 2002

Time:			2:20p.m.-4:20p.m.

Venue:			Room 1505
			Lifts 25-26

Committee Members:	Dr. Nevin Zhang (Supervisor)
			Dr. Fangzhen Lin (Chairman)
			Dr. James Kwok



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