Applying latent tree analysis to classify Traditional Chinese Medicine syndromes (Zheng) in patients with psoriasis vulgari

OBJECTIVE To treat patients with psoriasis vulgaris using Traditional Chinese Medicine (TCM), one must stratify patients into subtypes (known as TCM syndromes or Zheng) and apply appropriate TCM treatments to different subtypes. However, no unified symptom-based classification scheme of subtypes (Zheng) exists for psoriasis vulgaris. The present paper aims to classify patients with psoriasis vulgaris into different subtypes via the analysis of clinical TCM symptom and sign data. METHODS A cross-sectional survey was carried out in Beijing from 2005-2008, collecting clinical TCM symptom and sign data from 2764 patients with psoriasis vulgaris. Roughly 108 symptoms and signs were initially analyzed using latent tree analysis, with a selection of the resulting latent variables then used as features to cluster patients into subtypes. RESULTS The initial latent tree analysis yielded a model with 43 latent variables. The second phase of the analysis divided patients into three subtype groups with clear TCM Zheng connotations: 'blood deficiency and wind dryness'; 'blood heat'; and 'blood stasis'. CONCLUSIONS Via two-phase analysis of clinic symptom and sign data, three different Zheng subtypes were identified for psoriasis vulgaris. Statistical characteristics of the three subtypes are presented. This constitutes an evidence-based solution to the syndromedifferentiation problem that exists with psoriasis vulgaris

A Structural Equation Model Examining the Cultural Relevance of Physical Self-Concept and Self-Esteem in Chinese Children

Objectives: The purposes of this study were to investigate: (1) whether three subscale scores (i.e.,body fat, appearance, and strength) and the global physical self-concept and global self-concept scores of the Physical Self-Description Questionnaire (PSDQ) are applicable to Chinese children; (2) whether there is a gender difference in these relationships; and (3) whether global physical self-concept acts as a mediator of global self-concept. Method: 320 Chinese children aged 7-12 years were recruited. The PSDQ was used to assess two global dimensions (global physical self-concept and global self-concept) and three specific dimensions (body fat, appearance, and strength) of self-worth. The children’s version of the silhouette matching task (SMT) was adopted from Marsh and Roche (1996) to measure children’s body perception. Results: The factor structure of the modified PSDQ model was applicable in young Chinese children, regardless of their gender. Results of structural equation model (SEM) indicated that the model was acceptable. The structural paths of the model and the mediating effect of global physical self-concept on global self-concept were discussed. Conclusion: The present study indicates that the physical self is an increasingly important correlate of self-esteem. It is particularly important relative to perceived self-presentation and social acceptance. Therefore, it is important to consider the impact of the physical self-concept on global self-concept from both cultural and worldwide perspectives

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient's symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.Comment: 10 main pages + 2 reference pages, to appear at CCL202