Scientific Computing When Big data Meets Real World Clinical Research

Scientific Computing When Big data Meets Real World Clinical Research Guo-Zheng Li 1,2, Xue-Wen Zuo 2 and Baoyan Liu 1* 1 China Academy of Chinese Medical Science, Beijing 100700, China 2 Department of Control Science, Tongji University, Shanghai 201804, China First author: Guo-Zheng Li,gzli@tongji.edu.cn Phone: 15021800128 * Corresponding author: Baoyan Liu, liuby@mail.cintcm.ac.cn Running title: Real world big clinical data Keywords: Big data, real world, clinical research, Chinese medicine, Medical computing

Abstract: Traditional Chinese Medicine (TCM) confronts both opportunities and challenges in advent of big data era. Under the guidance of real world clinical scientific research paradigm of TCM, the paper illustrates the origin, concept, connotation and value of scientific computing studies in TCM, and discusses the integration of science, technology and medicine. Particularly, the TCM clinical diagnosis,treatment process and knowledge that were traditionally limited to literature and sensation level are converted into statistics by primary methods such as feature subset optimizing, multi-label learning and complex networks on the basis of complexity science, intelligence science, data science and computing science. Furthermore, it is applied in the modeling, analysis and decision to deal with various complex relationships of clinical individualized diagnosis and treatment, providing a powerful tool support for the real world clinical research paradigm of TCM.

1. Introduction In recent decades, when researchers in the field of data science advocate the value of the data and the significance of data mining, special issues on the big data have been published by both Nature and Science [1-2]. They demonstrate the universality of big data in every scientific field and the requirements of data processing from a newer and higher prospective, which promises the arrival of the era of big data. Similarly, there is also a demand of the large data in the field of TCM. Especially under the leadership of the state administration of traditional Chinese medicine, the establishment of clinical research sharing systems from Chinese clinical research base of TCM [3] organized by CACMS (China Academy of Chinese Medical Science), along with the electronization of hospital information system and the advancement of the Internet of things [4], brings about urgent needs on big data of clinical research. Meanwhile, national data center of traditional Chinese medicine in the construction also requires related theory and technical support [5]. Recently real world of traditional Chinese medicine clinical research paradigm [6] published by journal traditional Chinese medicine has aroused heated echo, since it responses to the dilemma clinical research lies in currently and predict development trend for TCM and even the whole medical science. "Data oriented", "Combination of medical practice and scientific computing" are highlighted in the real-world clinical scientific research paradigm, indicating an urgent demand for data computing in TCM. The collection, management, analysis and demonstration of big data as well as some techniques and methods such as complexity science [7], intelligent science [8], data science [9] and computing science have been applied more or less and obtained achievements worthy of reference in the field of TCM. Accordingly, based on these methods and guided by the real world clinical research paradigm,

this paper illustrates systematically the concept, connotation, value, research fields and calculation methods of scientific computing in the study of TCM. 2. Discipline of Scientific Computing in TCM. The real-world clinical research paradigm of TCM is human centered, data oriented and problems driven with medical practice alternated with scientific computing, clinical practice integrated with scientific research [6]. It inherits from the typical model of traditional Chinese medicine research and combines concepts, theories and techniques including modern clinical epidemiology, evidence-based medicine, complexity science, intelligence science, data science, statistics and information science. For the full utility of the nascent theories and techniques brought up by data science and intelligence science, data oriented is both the key and the premise of the real-world clinical research paradigm in current trend of science and technology, which means all kinds of real-world clinical diagnosis and treatment information needs to be comprehensively collected and converted into data. Ancient literature data, modern literature data, future clinical data, and numerous biology experimental data such as genomics, proteomics and metabolomics data, as well as the daily living-data related with human body health obtained from the Internet of things. Formation and consolidation of these data will become a reality, opening up a new prospect on clinical research of TCM with big data supported. From this perspective, "data oriented" will be the inevitable passage to clinical research of traditional Chinese medicine, the organic combination of western medicine and traditional Chinese medicine, the key technology of advantages complementing and the premise of real-world clinical research paradigm of TCM. "Medical practice and scientific computing alternated" is the main form of the real world clinical research paradigm of TCM and a contemporarily essential approach to "from clinic, to clinic".

Scientific computing becomes a sharp tool in the era of big data. Now scientific computing, to a certain extent, supersedes people to remember, analysis on the data of clinical medical practice, and furthermore can obtain more accurate and comprehensive laws and knowledge. However, whether from human intelligence or computer processing, the results should be verified from clinical validation and practice. Thus scientific computing should alternate with medical practice. With the premise and key technology "data oriented", and main form "medical practice alternated with scientific computing" in real-world clinical scientific research in TCM, the impending demand of data science, information science, intelligence science, complexity science, computing science has been revealed. There is need to relate disciplines of TCM with the characteristics of traditional Chinese medicine to solve the problem such as data acquisition, analysis, management, validation. Instead of applying these techniques simply and separately, a indiscipline subject is required for two reasons: first, for a clear understanding of the data characteristic of traditional Chinese medicine theory and technical problems second, for deep research in the data, information, intelligent, complexity science and technology fields,. Therefore, according to the demand of the data analysis, applicable theory and technology can be developed and proposed, and the research into real-world clinical scientific research in TCM fulfilled. Scientific computing of TCM is catering to the requirement. We can treat it from two perspectives: the application into the clinical research of complex science, intelligence science, data science, and information science, or the use and embedment in the related computing subjects o f the thinking, theory and knowledge or more specific technique of TCM which in turn improves the efficiency and the level of clinical research. The former is limited to the technological level but has developed for a period, while the latter is nascent with a deeper sense of interdisciplinary involved.

From the "disease - symptom - syndrome - prescription - effect" framework, both symptoms objective collection and structured entry in TCM require scientific computing as the basis for further analysis of data mining. For inspection symptoms, image processing and pattern recognition technology can be needed for information acquisition such as color of tongue and facial texture, color and luster [10-11]; as for auscultation and smelling clinical symptoms, signal processing, voice analysis and pattern recognition technology are used to obtain the information such as voice, cough, breath, body odor [12, 13]; the optimization of inquiry scale draws support from machine learning techniques [14]; Pulse-taking is more need to vibration signal analysis and pattern recognition techniques to obtain pulse rate, pulse rhythm, pulse force, pulse pattern and even pulse type [15]. Based on numerous clinical symptoms, feature selection and classification modeling of data mining can optimize these symptoms and obtain the optimal subset, and the simulation modeling from symptoms to syndrome [16]. Complex network and association rule can be applied in the core prescription mining and its addition or subtraction [17]. Use drug effects to discover significant interactions from TCM patient prescription data [18]. These concerns the application of current intelligent data processing technology into the analysis of the data of Chinese medicine. Considering the lack of relative study of information technology combined with TCM thinking and data characteristics, the Yin Yang and the five elements theory is applied to machine learning, which brings up the BYY intelligent system [19]. Considering the mixture of TCM syndromes, the multi label learning is proposed in syndrome diagnosis, effectively improving the accuracy of diagnosis of syndromes [20]. As can be viewed, scientific computing of TCM is specialized in scientific computing of the real world of TCM clinical research paradigm, which has some relations with TCM informatics and TCM

engineering in clinical research. TCM Informatics is an emerging discipline from the Chinese medicine and information science, which is based on the movement laws of dynamic phenomena, to follow the overall criterion and dynamic criterion, use of computer and network technology. It studies on the information phenomenon of TCM field and information law, fulfilling the exhibition, management, analysis, simulation and dissemination of information on traditional Chinese medicine aiming to acquire, convert and share the information that reveals the essence and internal relations [21]. Scientific computing of TCM overlaps with the traditional Chinese medicine. Firstly, as the center of computing, data is conceptually broader than information, and computing in TCM focuses on clinical data. Secondly, TCM is directed by theories of complexity science, intelligence science, data science and computing science. It concentrates on data collection, analysis and mining in the clinical course, in order to discover and establish rules and system of individualized clinical diagnosis and treatment. Under the guidance of theory in TCM, TCM engineering synthetically employs the theories, methods, techniques of modern natural science and engineering science. In areas of theoretical system, experimental research, clinical care, education, scientific research, production and management decision-making, TCM engineering develops exhaustive further study from all aspects: interdisciplinary, multi-method, multi-approach, multi-tools, multi perspectives (macro and micro). In the construction of versatile technology platform, all kinds of problems about theories, techniques and practices can be solved during the developing of TCM, which promotes the modernization, industrialization and internationalization of TCM [22], and makes a contribution for life science and human health. Compared with the traditional computing science, TCM engineering puts more emphasis on the engineering view of study, scientific computing of TCM analyses and mines theories and

techniques of TCM clinical diagnosis and treatment system in the view of complexity science and data science. The age of big data, a large number of data have emerged in various scientific fields, in urgent demand for scientific computing, considering systems and data characteristics in biological and social study, biological computing and social computing [23] appeared successively, the research direction puts theories and techniques of data science into the biological and social data analysis. The mechanism of biological and social systems helps to improve the computational efficiency and results. Thanks to the advancement of these disciplines, scientific computing of TCM will thrive by absorbing the commonly advantageous technology, and furthermore promote the development of clinical research in traditional Chinese medicine. 3 Theoretical framework of scientific computing for TCM As biological, social and other complex systems, scientific computing in TCM is involved with human, Chinese medicine and relative medical knowledge. Consequently, by use of comparatively limited resources, in essence, it is impossible to determine the entire behavior of the whole system of TCM by independent analysis of individual parts, either predict behavior in a wide range of time or space. This shows that we ought to study the scientific computing in TCM by holism rather than reductionism. Additionally, comprehensive samples and correlations in the big data are the main characteristics of the TCM computing. Notice that: (a) It is important to hold the holistic views of traditional Chinese medicine; (b) There does not exist the optimal solution in the general sense, not to mention exclusive optimal solution. Therefore, we should accept any effective solution. Above all, under the guide of theses principles "from clinic to clinic", "continuous exploration and improvement", in reference to the

social computing theory and technology [24], we seek effective solutions of scientific computing in TCM by use of digital human body system, objective collection, expert system, knowledge engineering, parallel system, complex system science and data mining as the theoretical framework of scientific computing for TCM in Figure 1. Digital Body System Symptom Collection System Knowledge System Parallel System Traditional Chinese Medical System Statistics Data Mining Machine Learning Complex Network Expert System Knowledge Engineering System Integration Figure 1 Theoretical framework of scientific computing for TCM (1) Expert system, knowledge engineering, parallel system Traditional Chinese medical system is extremely complex, real world clinical research paradigm proposed "human centered", which includes both patients and doctors, with both individual independence and interactions. So far, it is hard for effective methods and models to describe the behavior. In this respect have individual and the relationship of digital human body modeling, objective collection, expert system, knowledge engineering to model the relationship between the doctors and patients. The substance composition, mechanical model, mathematical model and information model of human body system are given by the digital human body system [25]. Objective collection system simulates the acquisition of four diagnostic symptoms of TCM using images, sounds, smell, and pressure sensor [26]. On this basis, knowledge rule, reasoning, and artificial neural network can be employed to build the accessorial diagnosis& treatment expert system about medical disease, chronic

hepatitis, and sub-health [27], and further facilitate the establishment of TCM knowledge engineering research and system [28]. The expert system, knowledge engineering and practical system constitute TCM parallel system [24]. Through mutually corresponding and comparison of artificial and practical diagnosis and treatment behavior in the parallel system, the control and management of the clinical scientific research can be implemented. TCM computing parallel system fundamentally makes use of the connection between practical system and artificial system, and accomplishes comparison and analysis to fulfill the "reference" and "prediction" about their future status. Accordingly, it will adjust the approach of controlling and management to get effective solutions to complex problems or to solve implementation issues on learning and training objectives. (2) Big data analysis and mining At present, most of the scientific computing in TCM adopts the methods of passive observation and statistics, it is sometimes difficult to perform experiments on the research objects initiatively, not to mention repetitious experiment. Under the guide of controlled randomized trials currently with countless the uncontrollable and unobservable factors, tests often make results and conclusions far from generalized. It has been inaccessible to analyze any computing problem in TCM by use of analytic reasoning method, so the method of data mining, machine learning and pattern recognition is an important direction in the big data analysis and mining. Rules and knowledge acquisition in expert system and knowledge engineering as mentioned above, as well as traditional expert inquiry have proved little feasible. In these clinical tasks such as the core optimization and syndrome-effect analysis, it is particularly significant to study on feature selection, classification, clustering, rule extraction and complex network technology.

Traditional Chinese medicine is a discipline directed by the real world TCM clinical research paradigm, and based on complexity science, intelligence science, data science and computing science. In the theoretical frame mentioned previously TCM computing methods absorb distinguished advantages from various subjects, including: (1) As the most fundamental method in computing, mathematical statistics has played a vital role in the previous study of the traditional Chinese medicine. Bionic optimization method designs optimized techniques based on mechanism of biological transporting, which can complete tasks such as symptom optimization, core optimization and path optimization. (2) Data mining techniques are effective to discover the underlying knowledge in the big data. Feature extraction and selection method is used to extract the essential features that data contains, helpful to discover knowledge and rules behind the data, and explore core symptoms as well. Classification modeling method can not only be used for the simulation of clinical thinking but help to obtain the doctor's diagnosis experience and knowledge. Association rules method can be used in mining and diagnosis to find out the relationship among symptoms, syndrome and prescription drugs, and the most relevant relationship of the disease - symptom - syndrome - prescription - effect. (3) TCM is a complicated system; therefore complex network method can be used to study the complex relationship of the disease - symptom - syndrome - prescription - effect, which is either directly related or nonlinear. The method of system dynamics can be used to simulate the process of disease attacking, in order to study the occurrence and development of different diseases. (4) Expert systems and knowledge engineering methods can construct the system to simulate the process of diagnosis, further finding out the treatment and medical mechanism. (5) The method of integration assembles these methods, on the purpose to explore individualized

diagnosis and treatment of traditional Chinese medicine. 4. Application fields of scientific computing in TCM Scientific computing in TCM is a material bonding of science computing and Chinese medicine field after biological computing and social computing. On the basis of Chinese medicine, complexity science, intelligence science, data science and computing science, the research and application of traditional Chinese medicine is conducted by extracting quintessential from complex and redundant data, from the view of treatment, explore the interaction between human internal and external factors and the relationship among etiology, pathogenesis, disease location and complex states. Some aspects are as follows: 1) Medical equipment for symptoms collection. With the aid of wearable computing devices, information from multiple dimensions can be acquired such as time, location, environment, physiological signals and motion signals. Big data of human body can be collected through continuously monitoring multi-dimensional signals for a long period. These health related information will deliver huge value and have a promising market outlook. Four diagnostic instruments in TCM consist of basic four acquisition parts: inspection, auscultation, inquiry and palpation [10-11]. 2) Structured knowledge system for electronic health record collection. Clinical data is the firsthand and vital evidence for Traditional Chinese Medicine (TCM) clinical research. Plentiful empirical knowledge is integrated into the clinical data of high-experienced TCM practitioner, which is proved remarkable therapeutic effects [3,29]. 3) Symptom interactions analysis. Discover the relationship between each kind of symptoms especially to explore the core symptoms subset. Most of the existing work of Chinese medicine research based on machine learning does not think over the correlations between medical connotation

and symptoms behind data. However, a large amount of symptoms and syndromes data of TCM has its corresponding clear medical meaning. Thus, it is significant to study the interactions between symptoms and syndromes and TCM knowledge behind these associations [14, 30]. 4) Symptom and syndrome (disease) correlation analysis. The correlation analysis can be implemented in diagnosis modeling. In practical data mining tasks of TCM, numerous clinical cases may have a certain one of various syndromes; this task can be regarded as a multi label classification problem in machine learning. The existing solutions on the multi label classification pay less attention to the problem of unbalanced data and label inconsistency [16, 20]. 5) Analysis of the core prescription and patent medicine addition &subtraction. Find out effective cure to a disease, and its addition and subtraction. There are plenty of literatures in the history and recent years, which have much valuable knowledge needed to be discovered [31]. 6) Analysis of prescription and efficacy relationship. Analyze the matching of prescription and efficacy assessment. Traditional Chinese medicine has been testified itself by clinical efficacy. The utility of different prescriptions is a hotspot in TCM research, making preparations for the progress of traditional Chinese medicine. Lu et al. collected nearly 6000 real cases and prescription effects from hospital during the pandemic period. Though analysis on these cases, found that the traditional Chinese medicine has advantages over Western medicine in pandemic fever treatment, and when accompanied with Western medicine only a few combinations have good curative effect [32]. 7) Analysis of data from biomedical instruments. Mars500 study was a kind of psychology and physiology isolation experiment conducted by Russia, the European Space Agency and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission.

The experiment permitted the study of the technical challenges, work capability of crew and management of long-distance spaceflight. Health problems, conditions of isolation and hermetically closed, confined environment, are the main peculiarities of the Martian flight. Li et al. adopt statistics method to describe the syndrome factor regular pattern and present machine learning methods to mine the relationship between computerized symptoms and expert differentiation syndromes. With feature selection, they screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multi-label classification model reaches to 80% [33]. 8) Application of bioinformatics technology in TCM. Lu et al. worked on the curative effect of the medicine etretinate from the comparison between health and psoriasis patients and their treatment cycles by using metabolomics technology, and furthermore found metabolomics tech can be well applied to analyze the effect of Chinese medicine [34]. 5. Conclusions and Perspectives In this paper, considering the characteristics of big data, the key technology of the real-world clinical research paradigm in TCM is illustrated comprehensively as data oriented, and the main form as medical practice and scientific computing alternating. Furthermore, the research directions about scientific computing in TCM have been proposed. The direction is beneficial to gather more research strength concentrating on problems and promote inter-cooperation between the clinical researchers of TCM and information experts. The main research contents and methods of the different levels are demonstrated in the direction. Scientific computing in TCM has its special characteristics, distinguished from other scientific computing, here we would like to name a new branch for TCM or scientific computing, i.e. TCM computing or Chinese Medical Computing. Study of TCM computing has developed based on a certain

foundation, but we need to take the development of real-world clinical research paradigm into account In-deep study on clinical research process requires science computing to fulfill solutions and advancements. It is advisable to study the computing methods that satisfy the characteristics of Chinese medicine data. E.g. As big data is accumulated in an unexpected way, deep learning is now becoming a popular technique to utilize the big data. While there are special requirements in the analysis of TCM data, we need to invent novel algorithms or revise the existing algorithms of deep learning when applied to TCM data. Hope the research direction could facilitate the implementation and advancement of real-world clinical research paradigm, so as to thrive TCM and contribute to the development of medical field. Acknowledgments This work was supported by the Natural Science Foundation of China under grants no. 61273305 and the Fundamental Research Funds for the Central Universities. Conflict of interest Guo-Zheng Li, Xuewen Zuo and Baoyan Liu declare that they have no conflict of interest. References [1]. Special Online Collection: Dealing with data. Science, Feb. 2011. http://www.sciencemag.org/site/special/data/ [2]. Big data: Science in the petabyte era. Nature, 2008. http://www.nature.com/nature/journal/v455/n7209/edsumm/e080904-01.html [3]. B. Y. Liu, X. Z. Zhou, P. Li(2007). A Unified Clinical and Research Information Platform toward Individualized Medicine. China Digital Medicine,6, 020. [4]. L. Yu, Y. Lu, Y. M. Tian, X. L. Zhu, (2012). Research on architecture and key technology of

internet of things in hospital. Transducer and Microsystem Technologies, 6, 023. [5]. B. Y. Liu, Big data rendering Contemporary TCM nautical chart. China TCM newspaper, June 5, 2013 (3) (in Chinese) [6]. B. Y. Liu. The real world of TCM clinical research paradigm. Journal of traditional Chinese medicine, 2013, 54 (6): 451-455 (in Chinese). [7]. X. R. Huang. Complexity science. Chongqing University,2012. [8]. Z. Shi, Intelligence Science, Tsinghua University Press, 2006 [9]. G. -Z. Li, X. Q. Zeng. Research progress in Chinese clinical medical data analysis and mining, International Journal of Biomedical Engineering, 2013, 36(2): 88-92. [10]. F. Li, C. Zhao, Z. Xia, Y. Wang, X. Zhou, G.-Z. Li, Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines, BMC Complementary and Alternative Medicine, 2012, 12:127. [11]. M.-J. Shi, G.-Z. Li, F. Li, C. Xu. Computerized tongue image segmentation via the double geo-vector flow, Chinese Medicine,2014, 9:7 doi:10.1186/1749-8546-9-7. [12]. Y. T. Gao, Wenzhen,Peking: Chinese Ancient Resources, 2008. [13]. D. Guo, D. Zhang. A Novel Breath Analysis System Based on Electronic Olfaction [J]. IEEETRANSACTIONS ON BIOMEDICAL ENGINEERING, 2010,57:11. [14]. H. Shao, G.-Z. Li. Symptom Selection for Multi-label Data of Inquiry Diagnosis in Traditional Chinese Medicine, science China Information Sciences, 2013,56(5): 1-13. [15]. X.Huang, W.Tang, R. Li;H. Huang. Comparative Study on Synchronous Test of Three-Position Pulse and Simple Pulse Test, Chinese Journal of Basic Medicine in TCM,2005,11(3): 210-234. [16]. M. You, G.-Z. Li. Chapter x, Medical Diagnosis by Using Machine Learning Techniques, In:

Josiah Poon and Simon Poon (Editors), Data Analytics for Traditional Chinese Medicine Research, Springer, 2014. pp.39-80. (ISBN 978-3-319-03801-8). [17]. X. Z. Zhou,B. Y. Liu, Y. Wang.et.al. Study of compound drugs based on complex network method. Chinese Journal of information on TCM,2008, 15(11): 98-100. [18]. Simon Poon. A Novel Approach in Discovering Significant Interactions from TCM Patient Prescription Data. IJDMB, 2011, 5(4):353-68. [19]. Lei XU. On essential topics of BYY harmony learning: Current status, challenging issues, and gene analysis applications. Frontiers of Electrical and Electronic Engineering, 2012, 7(1):147-196. [20]. G.-P. Liu, G.-Z. Li, Y.-L. Wang, Y.-Q. Yang. Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning. BMC Complementary and Alternative Medicine, 2010, 10:37. [21]. M. Cui, A. N. Yin, H. Li. The establishment of Chinese medicine information study. Journal of Traditional Chinese Medicine,2008,49(3): 267-278. [22]. Z. Shi, S. Zhao. New Progress of Study in Engineering Area of Traditional Chinese Medicine [J]. 2005. [23]. Y. Jin, G. Hu, K. Wang. Computational biology: analysis and applications of biological sequence. Science Press,2010-03. [24]. F.-Y. Wang. Social Computing: Science and technology, Humanities. Bulletin of Chinese Academy of Science,2005,20(5): 370-376. [25]. S.-W. Bi, Digital human body-human body digital science, Science Press, 2004. [26]. Q. Wang. Study on the current situation of four diagnostic methods of TCM. Journal of

Traditional Chinese Medicine,2000,41(4): 242-245. [27]. D. Z. Zhang,J. N. Peng,H. X. Fan. Present research situation and prospect of Chinese medicine expert system, Application Research of Computers,2007,24(12): 6-9. [28]. Y. Sun. Research progress analysis of Chinese medical knowledge engineering, Chinese Journal of Information on Traditional Chinese Medicine,2010, 17(12): 5-6. [29]. M. You, Z. Wang, S. X. Yan, X.Q. Zeng, G. Z. Li, L. Xu, S. Huang. An Intelligent framework for Clinical Cases Management and Analysis in Traditional Chinese Medicine, BMC CAM S4, 2014 [30]. G. Z. Li, S. Sun, M. You, Y.-L. Wang, and G. P. Liu, Inquiry diagnosis of coronary heart disease in Chinese medicine based on symptom-syndrome interactions, Chinese Medicine, vol. 7, article 9, Apr. 2012. [31]. S. Y.Huang, S. C. Fang, H. Liu, R. Zhang, C. Y. Wang, L. Bi, L.Zhang, L. Xu, G. Z. Li, H. L. Wang, L. N. Su, the technology to carry out the old doctor of traditional Chinese medicine academic experience inheritance of global design examples of the application of data mining, Journal of Shanghai traditional Chinese medicine, Vol 45, No. 9, pp. 1-3, 2011 (in Chinese) [32]. C. Lu, H. Zhou, Y. Luo, B. Chen, X. Qin, Z. Wen, J. Li, A.H. Ou, W. Ouyang, X.Li, T.Huang, Z.Liang, S.Yan, G.-Z. Li. Comparison of the Clinical Outcomes of Chinese, Western, and Integrative Medicine for the Treatment of H1N1 Influenza A in China, BMC CAM S13, 2014 [33]. Y. Li, G.-Z. Li, J.Y. Gao, Z. Zhang, Q. Fan, J. Xu, G. Bai, K. Chen, H. Shi, S. Sun, Y. Liu, F. Shao, T. Mi, X. Jia, S. Zhao, J. Chen, J. Liu, Y. Guo. Syndrom differentiation analysis on MARS500 data of traditional Chinese medicine, BMC CAM S16, 2014 [34]. C. Lu, J. Deng, L. Li, G.-Z. Li. Application of Metabolomics on Diagnosis and Treatment of

Patients with Psoriasis in Traditional Chinese Medicine, BBA - Proteins and Proteomics, SI: Comp Proteomics, Sys Biol., Clin Impl, 2014, 1844(1), Part B:280-288.