Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis

Fu, Chen; Zhang, Nevin L.; Chen, Baoxin; Chen, Zhou Rong; Jin, Xiang Lan; Guo, R. S.; Chen, Zhigang; Zhang, Yunling

doi:10.1016/s2095-4964(17)60335-2

Cited by 16 publications

(17 citation statements)

References 17 publications

(33 reference statements)

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“…The data set involves 803 patients and 93 symptoms. Detailed information about the data set and the data collection process is given in [1].…”

Section: Symptom Pattern Discoverymentioning

confidence: 99%

“…An examination of all the latent variables in Figure has found that Y02, Y05, Y09, Y10, Y12, Y14, Y15 and Y25 fall into the second category, whereas the other latent variables fall into the first category. The details are given in [1].…”

Section: Symptom Pattern Discoverymentioning

confidence: 99%

“…The TCM syndrome classification problem associated with a WM disease consists of four subproblems: (1) What TCM syndrome types are there among the patients with the disease? (2) What is the prevalence of each syndrome type?…”

Section: Introductionmentioning

confidence: 99%

“…Related works will be discussed in Section 9, and conclusions drawn in Section 10. A data set on vascular mild cognitive impairment (VMCI) [1] will be used for illustration throughout the paper.…”

Section: Introductionmentioning

confidence: 99%

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A data-driven method for syndrome type identification and classification in traditional Chinese medicine

Zhang

Liu

et al. 2017

Journal of Integrative Medicine

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The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.

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“…The data set involves 803 patients and 93 symptoms. Detailed information about the data set and the data collection process is given in [1].…”

Section: Symptom Pattern Discoverymentioning

confidence: 99%

Section: Symptom Pattern Discoverymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A data-driven method for syndrome type identification and classification in traditional Chinese medicine

Zhang

Liu

et al. 2017

Journal of Integrative Medicine

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“…Latent structure analysis [4][5][6][7][8][9], has become one of the most successful unsupervised learning methods for TCM syndrome differentiation. It provides standardization and objectiveness for syndrome differentiation when there is no reference standard.…”

Section: Introductionmentioning

confidence: 99%

CNN based Multi-Instance Multi-Task Learning for Syndrome Differentiation of Diabetic Patients

Wang

Sun

Poon

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Syndrome differentiation in Traditional Chinese Medicine (TCM) is the process of understanding and reasoning body condition, which is the essential step and premise of effective treatments. However, due to its complexity and lack of standardization, it is challenging to achieve. In this study, we consider each patient's record as a one-dimensional image and symptoms as pixels, in which missing and negative values are represented by zero pixels, labeled by one or more syndromes in diabetes. The objective is to find relevant symptoms first and then map them to proper syndromes, that is similar to the object detection problem in computer vision. Inspired from it, we employ multi-instance multi-task learning combined with the convolutional neural network (MIMT-CNN) for syndrome differentiation, which takes region proposals as input and output image labels directly. The neural network consists of region proposals generation, convolutional layer, fully connected layer, and max pooling (multi-instance pooling) layer followed by the sigmoid function in each syndrome prediction task for image representation learning and final results generation. On the diabetes dataset, it performs better than all other baseline methods. Moreover, it shows stability and reliability to generate results, even on the dataset with small sample size, a large number of missing values and noises.

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Herbal medicine for inflammatory bowel diseases: development of pattern identification algorithms by retrospective analysis of case series data

Won

Lee

Jung

et al. 2020

European Journal of Integrative Medicine

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Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis

Cited by 16 publications

References 17 publications

A data-driven method for syndrome type identification and classification in traditional Chinese medicine

A data-driven method for syndrome type identification and classification in traditional Chinese medicine

CNN based Multi-Instance Multi-Task Learning for Syndrome Differentiation of Diabetic Patients

Herbal medicine for inflammatory bowel diseases: development of pattern identification algorithms by retrospective analysis of case series data

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