Latent variable models and factors analysis
Latent variable models and factors analysis
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical latent class models for cluster analysis
Eighteenth national conference on Artificial intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Efficient Learning of Hierarchical Latent Class Models
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Artificial Intelligence in Medicine
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Dimension correction for hierarchical latent class models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine
Computer Methods and Programs in Biomedicine
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
The role of operation granularity in search-based learning of latent tree models
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
Discovery of regularities in the use of herbs in traditional chinese medicine prescriptions
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo
Computer Methods and Programs in Biomedicine
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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Objective: TCM (traditional Chinese medicine) is an important avenue for disease prevention and treatment for the Chinese people and is gaining popularity among others. However, many remain skeptical and even critical of TCM because of a number of its shortcomings. One key shortcoming is the lack of objective diagnosis standards. We endeavor to alleviate this shortcoming using machine learning techniques. Method: TCM diagnosis consists of two steps, patient information gathering and syndrome differentiation. We focus on the latter. When viewed as a black box, syndrome differentiation is simply a classifier that classifies patients into different classes based on their symptoms. A fundamental question is: do those classes exist in reality? To seek an answer to the question from the machine learning perspective, one would naturally use cluster analysis. Previous clustering methods are unable to cope with the complexity of TCM. We have therefore developed a new clustering method in the form of latent tree models. We have conducted a case study where we first collected a data set about a TCM domain called kidney deficiency and then used latent tree models to analyze the data set. Results: Our analysis has found natural clusters in the data set that correspond well to TCM syndrome types. This is an important discovery because (1) it provides statistical validation to TCM syndrome types and (2) it suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation. In this paper, we provide a summary of research work on latent tree models and report the aforementioned case study.