Table of Contents Author Guidelines Submit a Manuscript
Evidence-Based Complementary and Alternative Medicine
Volume 2015 (2015), Article ID 376716, 18 pages
http://dx.doi.org/10.1155/2015/376716
Review Article

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Department of Control Science and Engineering, Tongji University, Shanghai 201804, China

Received 7 November 2014; Accepted 7 April 2015

Academic Editor: Angelo A. Izzo

Copyright © 2015 Changbo Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Lukman, Y. He, and S.-C. Hui, “Computational methods for traditional chinese medicine: a survey,” Computer Methods and Programs in Biomedicine, vol. 88, no. 3, pp. 283–294, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Maciocia, The Foundations of Chinese Medicine, Churchill Livingstone, 1989.
  3. J. Jokiniemi, Ontologies and computational methods for traditional Chinese medicine [M.S. thesis], 2010.
  4. M. Dodd, S. Janson, N. Facione et al., “Advancing the science of symptom management,” Journal of Advanced Nursing, vol. 33, no. 5, pp. 668–676, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Jiang, C. Lu, C. Zhang et al., “Syndrome differentiation in modern research of traditional Chinese medicine,” Journal of Ethnopharmacology, vol. 140, no. 3, pp. 634–642, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Gu and H. Chen, “Modern bioinformatics meets traditional Chinese medicine,” Briefings in Bioinformatics, vol. 15, no. 6, pp. 984–1003, 2014. View at Publisher · View at Google Scholar
  7. A. Ferreira, “Advances in Chinese medicine diagnosis: from traditional methods to computational models,” in Recent Advances in Chinese Medicine, H. Kuang, Ed., InTech, Rijeka, Croatia, 2012. View at Google Scholar
  8. A. Lu, M. Jiang, C. Zhang, and K. Chan, “An integrative approach of linking traditional Chinese medicine pattern classification and biomedicine diagnosis,” Journal of Ethnopharmacology, vol. 141, no. 2, pp. 549–556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Zhang, X. Zhou, R. Zhang, B. Liu, and Q. Xie, “Real-world clinical data mining on TCM clinical diagnosis and treatment: a survey,” in Proceedings of the IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom '12), pp. 88–93, IEEE, Beijing, China, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. A. S. Ferreira and A. J. Lopes, “Chinese medicine pattern differentiation and its implications for clinical practice,” Chinese Journal of Integrative Medicine, vol. 17, no. 11, pp. 818–823, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Lan, Y. Lu, K. Jin, T. Zhu, and Z. Jin, “Data mining: a modern tool to investigate traditional chinese medicine,” Journal of US-China Medical Science, vol. 8, no. 5, pp. 316–320, 2011. View at Google Scholar
  12. X. Zhou, Y. Peng, and B. Liu, “Text mining for traditional Chinese medical knowledge discovery: a survey,” Journal of Biomedical Informatics, vol. 43, no. 4, pp. 650–660, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Guo, Y. Wang, F. Li et al., “Modernization of traditional Chinese medicine diagnosis based on modern information technologies,” in Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE '10), pp. 1–5, IEEE, Chengdu, China, June 2010. View at Publisher · View at Google Scholar
  14. A. Sá Ferreira, “Diagnostic accuracy of pattern differentiation algorithm based on Chinese medicine theory: a stochastic simulation study,” Chinese Medicine, vol. 4, article 24, 15 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Feng, Z. Wu, X. Zhou, Z. Zhou, and W. Fan, “Knowledge discovery in traditional Chinese medicine: state of the art and perspectives,” Artificial Intelligence in Medicine, vol. 38, no. 3, pp. 219–236, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Huang and N. Li, “Pixel based tongue color analysis,” in Medical Biometrics, vol. 4901 of Lecture Notes in Computer Science, pp. 282–289, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  17. C.-C. Chiu, “A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue,” Computer Methods and Programs in Biomedicine, vol. 61, no. 2, pp. 77–89, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. Y.-G. Wang, J. Yang, Y. Zhou, and Y.-Z. Wang, “Region partition and feature matching based color recognition of tongue image,” Pattern Recognition Letters, vol. 28, no. 1, pp. 11–19, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Zhang, B. Pang, N. Li, K. Wang, and H. Zhang, “Computerized diagnosis from tongue appearance using quantitative feature classification,” The American Journal of Chinese Medicine, vol. 33, no. 6, pp. 859–866, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Kanawong, T. Obafemi-Ajayi, J. Yu, D. Xu, S. Li, and Y. Duan, “ZHENG classification in Traditional Chinese Medicine based on modified specular-free tongue images,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '12), pp. 288–294, IEEE, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Zhi, D. Zhang, J.-Q. Yan, Q.-L. Li, and Q.-L. Tang, “Classification of hyperspectral medical tongue images for tongue diagnosis,” Computerized Medical Imaging and Graphics, vol. 31, no. 8, pp. 672–678, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. C. H. Siu, Y. He, and D. T. C. Thach, “Machine learning for tongue diagnosis,” in Proceedings of the 6th International Conference on Information, Communications and Signal Processing (ICICS '07), pp. 1–5, IEEE, Singapore, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Jang, J. Kim, K. Park, S. Park, Y. Chang, and B. Kim, “Development of the digital tongue inspection system with image analysis,” in Proceedings of the 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES 2002. Proceedings of the 2nd Joint Engineering in Medicine and Biology Conference, vol. 2, pp. 1033–1034, IEEE, 2002.
  24. H. Z. Zhang, K. Q. Wang, D. Zhang, B. Pang, and B. Huang, “Computer aided tongue diagnosis system,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 6754–6757, Shanghai, China, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Pang, D. Zhang, N. Li, and K. Wang, “Computerized tongue diagnosis based on Bayesian networks,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 10, pp. 1803–1810, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. Li, Y. Wang, H. Liu, Z. Sun, and Z. Liu, “Tongue fissure extraction and classification using hyperspectral imaging technology,” Applied Optics, vol. 49, no. 11, pp. 2006–2013, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. T. Watsuji, S. Arita, S. Shinohara, and T. Kitade, “Medical application of fuzzy theory to the diagnostic system of tongue inspection in traditional Chinese medicine,” in Proceedings of the IEEE International Fuzzy Systems Conference (FUZZ-IEEE '99), vol. 1, pp. 145–148, IEEE, August 1999. View at Scopus
  28. B. L. Pham and Y. Cai, “Visualization techniques for tongue analysis in Traditional Chinese Medicine,” in Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, vol. 5367 of Proceeding of SPIE, pp. 171–180, International Society for Optics and Photonics, February 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. B. Huang, J. Wu, D. Zhang, and N. Li, “Tongue shape classification by geometric features,” Information Sciences, vol. 180, no. 2, pp. 312–324, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. C.-C. Chiu, C.-Y. Lan, and Y.-H. Chang, “Objective assessment of blood stasis using computerized inspection of sublingual veins,” Computer Methods and Programs in Biomedicine, vol. 69, no. 1, pp. 1–12, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. Q. Li and Z. Liu, “Tongue color analysis and discrimination based on hyperspectral images,” Computerized Medical Imaging and Graphics, vol. 33, no. 3, pp. 217–221, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. X. Li, F. Li, Y. Wang, P. Qian, and X. Zheng, “Computer-aided disease diagnosis system in TCM based on facial image analysis,” International Journal of Functional Informatics and Personalised Medicine, vol. 2, no. 3, pp. 303–314, 2009. View at Publisher · View at Google Scholar
  33. F. Li, D. Di, X. Li et al., “Facial complexion acquisition and recognition system for clinical diagnosis in traditional chinese medicine,” in Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS '09), pp. 392–396, IEEE, Shanghai, China, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Liu and Z. Guo, “Hepatitis diagnosis using facial color image,” in Medical Biometrics, vol. 4901 of Lecture Notes in Computer Science, pp. 160–167, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  35. C. Liu, C. Zhao, G. Li, F. Li, and Z. Wang, “Computerized color analysis for facial diagnosis in traditional Chinese medicine,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '13), pp. 613–614, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. W. Tunhua, B. Baogang, S. Fushun, Z. Changle, and W. Ping, “Study on complexion recognition in TCM,” in Proceedings of the International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE '10), vol. 3, pp. 487–490, Chengdu, China, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. X. Wang, B. Zhang, Z. Guo, and D. Zhang, “Facial image medical analysis system using quantitative chromatic feature,” Expert Systems with Applications, vol. 40, no. 9, pp. 3738–3746, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. B. Zhang, X. Wang, F. Karray, Z. Yang, and D. Zhang, “Computerized facial diagnosis using both color and texture features,” Information Sciences, vol. 221, pp. 49–59, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. C. Zhao, G.-Z. Li, F. Li, Z. Wang, and C. Liu, “Qualitative and quantitative analysis for facial complexion in traditional Chinese medicine,” BioMed Research International, vol. 2014, Article ID 207589, 17 pages, 2014. View at Publisher · View at Google Scholar
  40. R. Zhou, L. Fu-Feng, W. Yi-Qin, X.-Y. Zheng, R.-W. Zhao, and G.-Z. Li, “Application of PCA and LDA methods on gloss recognition research in TCM complexion inspection,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '10), pp. 666–669, IEEE, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. B. Zhang, B. V. K. V. Kumar, and D. Zhang, “Noninvasive diabetes mellitus detection using facial block color with a sparse representation classifier,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1027–1033, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. L. Zheng, X. Li, X. Yan, F. Li, X. Zheng, and W. Li, “Lip color classification based on support vector machine and histogram,” in Proceedings of the 3rd International Congress on Image and Signal Processing (CISP '10), vol. 4, pp. 1883–1886, IEEE, Yantai, China, October 2010. View at Publisher · View at Google Scholar
  43. F. Li, C. Zhao, Z. Xia, Y. Wang, X. Zhou, and G.-Z. Li, “Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines,” BMC Complementary and Alternative Medicine, vol. 12, article 127, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. H. Wang, J. Yan, Y. Wang, F. Li, and R. Guo, “Digital technology for objective auscultation in traditional Chinese medical diagnosis,” in Proceedings of the International Conference on Audio, Language and Image Processing (ICALIP '08), pp. 1100–1104, IEEE, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. J. Yan, X. Shen, C. Xia et al., “Auscultation signals analysis in traditional Chinese medicine using wavelet packet energy entropy and support vector machines,” in Proceedings of the IEEE International Conference on Electrical and Control Engineering (ICECE '10), pp. 509–512, 2010.
  46. J. Yan, Q. Shen, Y. Wang et al., “Detecting non-stationarity for auscultation signal of traditional Chinese medicine,” Wuhan University Journal of Natural Sciences, vol. 16, no. 1, pp. 83–87, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Yan, Y. Shen, Y. Wang et al., “Nonlinear analysis of auscultation signals in traditional chinese medicine using wavelet packet transform and approximate entropy,” International Journal of Functional Informatics and Personalised Medicine, vol. 2, no. 3, pp. 325–340, 2009. View at Google Scholar
  48. C.-C. Chiu, H.-H. Chang, and C.-H. Yang, “Objective auscultation for traditional Chinese medical diagnosis using novel acoustic parameters,” Computer Methods and Programs in Biomedicine, vol. 62, no. 2, pp. 99–107, 2000. View at Publisher · View at Google Scholar · View at Scopus
  49. C.-C. Chiu, M.-T. Yang, and C.-S. Lin, “Using fractal dimension analysis on objective auscultation of traditional Chinese medical diagnosis,” Journal of Medical and Biological Engineering, vol. 22, no. 4, pp. 219–224, 2002. View at Google Scholar · View at Scopus
  50. J. Yan, Q. Shen, J. Ren et al., “A multi-instance multi-label learning approach to objective auscultation analysis of traditional Chinese medicine,” in Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI '11), pp. 1626–1630, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. J. Yan, Q. Shen, Y. Wang et al., “Multichannel speech signal enhancement method based on ICA for objective auscultation of traditional Chinese medicine,” in Proceedings of the IEEE International Symposium on IT in Medicine and Education (ITME '09), pp. 1097–1100, Jinan, China, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. J. Yan, H. Wang, C. Xia et al., “Nonlinear analysis in TCM acoustic diagnosis using delay vector variance,” in Proceeings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08), pp. 2099–2102, IEEE, Shanghai, China, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  53. J. Yan, Y. Shen, C. Xia, Y. Wang, F. Li, and R. Guo, “The objective auscultation research on traditional chinese medical using two novel parameters,” in Proceedings of the International Conference on Computer Design and Applications (ICCDA '10), vol. 1, pp. V1-572–V1-576, IEEE, Qinhuangdao, China, June 2010. View at Publisher · View at Google Scholar
  54. W. Ping, T. Yi, X. Haibao, and S. Farong, “A novel method for diabetes diagnosis based on electronic nose,” Biosensors and Bioelectronics, vol. 12, no. 9-10, pp. 1031–1036, 1997. View at Publisher · View at Google Scholar · View at Scopus
  55. J.-B. Yu, H.-G. Byun, M.-S. So, and J.-S. Huh, “Analysis of diabetic patient's breath with conducting polymer sensor array,” Sensors and Actuators, B: Chemical, vol. 108, no. 1-2, pp. 305–308, 2005. View at Publisher · View at Google Scholar · View at Scopus
  56. K. Yan, D. Zhang, D. Wu, H. Wei, and G. Lu, “Design of a breath analysis system for diabetes screening and blood glucose level prediction,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 11, pp. 2787–2795, 2014. View at Publisher · View at Google Scholar
  57. D. Guo, D. Zhang, N. Li, L. Zhang, and J. Yang, “Diabetes identification and classification by means of a breath analysis system,” in Medical Biometrics, vol. 6165 of Lecture Notes in Computer Science, pp. 52–63, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  58. D. Guo, D. Zhang, L. Zhang, and G. Lu, “Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis,” Sensors and Actuators B: Chemical, vol. 173, pp. 106–113, 2012. View at Publisher · View at Google Scholar · View at Scopus
  59. S. Dragonieri, R. Schot, B. J. A. Mertens et al., “An electronic nose in the discrimination of patients with asthma and controls,” The Journal of Allergy and Clinical Immunology, vol. 120, no. 4, pp. 856–862, 2007. View at Publisher · View at Google Scholar · View at Scopus
  60. M. Trincavelli, S. Coradeschi, A. Loutfi, B. Soderquist, and P. Thunberg, “Direct identification of bacteria in blood culture samples using an electronic nose,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 12, pp. 2884–2890, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. H. M. Saraoglu, A. O. Selvi, M. A. Ebeoglu, and C. Tasaltin, “Electronic nose system based on quartz crystal microbalance sensor for blood glucose and HbA1c levels from exhaled breath odor,” IEEE Sensors Journal, vol. 13, no. 11, pp. 4229–4235, 2013. View at Publisher · View at Google Scholar · View at Scopus
  62. M. Phillips, R. N. Cataneo, A. R. C. Cummin et al., “Detection of lung cancer with volatile markers in the breath,” Chest, vol. 123, no. 6, pp. 2115–2123, 2003. View at Publisher · View at Google Scholar · View at Scopus
  63. M. Phillips, N. Altorki, J. H. M. Austin et al., “Prediction of lung cancer using volatile biomarkers in breath,” Cancer Biomarkers, vol. 3, no. 2, pp. 95–109, 2007. View at Google Scholar · View at Scopus
  64. D. Guo, D. Zhang, N. Li, L. Zhang, and J. Yang, “A novel breath analysis system based on electronic olfaction,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 11, pp. 2753–2763, 2010. View at Publisher · View at Google Scholar · View at Scopus
  65. Y.-J. Lin, H.-R. Guo, Y.-H. Chang, M.-T. Kao, H.-H. Wang, and R.-I. Hong, “Application of the electronic nose for uremia diagnosis,” Sensors and Actuators B: Chemical, vol. 76, no. 1–3, pp. 177–180, 2001. View at Publisher · View at Google Scholar · View at Scopus
  66. P. Wang, W. Zuo, and D. Zhang, “A compound pressure signal acquisition system for multichannel wrist pulse signal analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 6, pp. 1556–1565, 2014. View at Publisher · View at Google Scholar · View at Scopus
  67. J.-H. Wu, R.-S. Chang, and J.-A. Jiang, “A novel pulse measurement system by using laser triangulation and a CMOS image sensor,” Sensors, vol. 7, no. 12, pp. 3366–3385, 2007. View at Publisher · View at Google Scholar · View at Scopus
  68. J.-H. Wu, W.-L. Lee, Y.-P. Lee et al., “An improved arterial pulsation measurement system based on optical triangulation and its application in the traditional Chinese medicine,” in Dimensional Optical Metrology and Inspection for Practical Applications, vol. 8133 of Proceedings of SPIE, p. 7, September 2011.
  69. J. S. Ni, W. Jin, B. N. Zhao et al., “Optic fiber pulse-diagnosis sensor of traditional chinese medicine,” in Fourth Asia Pacific Optical Sensors Conference, vol. 8924 of Proceedings of SPIE, Wuhan, China, October 2013. View at Publisher · View at Google Scholar
  70. J.-H. Lue, R. S. Chang, T.-C. Ko, Y.-S. Su, S. Cherng, and W.-M. Cheng, “Simple two-channel sound detectors applying to pulse measurement,” Life Science Journal, vol. 11, no. 4, pp. 421–423, 2014. View at Google Scholar · View at Scopus
  71. D. Zhang, W. Zuo, D. Zhang, H. Zhang, and N. Li, “Classification of pulse waveforms using edit distance with real penalty,” EURASIP Journal on Advances in Signal Processing, vol. 2010, Article ID 303140, 2010. View at Publisher · View at Google Scholar · View at Scopus
  72. H. Wang, “A quantitative method for pulse strength classification based on decision tree,” in Proceedings of the International Symposium on Information Science and Engineering (ISISE '08), vol. 2, pp. 111–115, IEEE, Shanghai, China, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  73. D. Jia, D. Zhang, and N. Li, “Pulse waveform classification using support vector machine with gaussian time warp edit distance kernel,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 947254, 10 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  74. L. S. Xu, M. Q.-H. Meng, and K. Q. Wang, “Pulse image recognition using fuzzy neural network,” in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '07), pp. 3148–3151, IEEE, Lyon, France, August 2007. View at Publisher · View at Google Scholar
  75. R. Wang, C. Feng, and X. Wang, “Pulse of human body identification base on fuzzy neural networks,” in Proceedings of the IEEE/ICME International Conference on Complex Medical Engineering (CME '07), pp. 340–343, IEEE, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  76. C. Ma, C. Xia, Y. Wang, H. Yan, and F. Li, “An improved approach to the classification of seven common TCM pulse conditions,” in Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI '11), vol. 2, pp. 621–624, IEEE, Shanghai, China, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  77. H.-Y. Wang and P.-Y. Zhang, “A model for automatic identification of human pulse signals,” Journal of Zhejiang University SCIENCE A, vol. 9, no. 10, pp. 1382–1389, 2008. View at Publisher · View at Google Scholar · View at Scopus
  78. H. Wang and Y. Cheng, “A quantitative system for pulse diagnosis in traditional Chinese medicine,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (EMBS '05), pp. 5676–5679, September 2005. View at Scopus
  79. W. Yang, L. Zhang, and D. Zhang, “Wrist-pulse signal diagnosis using ICpulse,” in Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '09), pp. 1–4, IEEE, Beijing, China, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  80. L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Classification of wrist pulse blood flow signal using time warp edit distance,” in Medical Biometrics, vol. 6165 of Lecture Notes in Computer Science, pp. 137–144, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  81. S. Gong, B. Xu, G. Sun et al., “Accurate cirrhosis identification with wrist-pulse data for mobile healthcare,” in Proceedings of the 2nd ACM Workshop on Mobile Systems, Applications, and Services for HealthCare (mHealthSys '12), p. 6, ACM, November 2012. View at Publisher · View at Google Scholar · View at Scopus
  82. Y. Sun, B. Shen, Y. Chen, and Y. Xu, “Computerized wrist pulse signal diagnosis using kpca,” in Medical Biometrics, vol. 6165 of Lecture Notes in Computer Science, pp. 334–343, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  83. X. Jiang, D. Zhang, K. Wang, and W. Zuo, “Distinguishing patients with gastritis and cholecystitis from the healthy by analyzing wrist radial arterial doppler blood flow signals,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 2492–2495, IEEE, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  84. D. Wang, D. Zhang, and J. C. Chan, “Feature extraction of radial arterial pulse,” in Proceedings of the International Conference on Medical Biometrics (ICMB '14), pp. 41–46, IEEE, Shenzhen, China, May 2014. View at Publisher · View at Google Scholar
  85. Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Computerized wrist pulse signal diagnosis using modified auto-regressive models,” Journal of Medical Systems, vol. 35, no. 3, pp. 321–328, 2011. View at Publisher · View at Google Scholar · View at Scopus
  86. D. Jia, N. Li, S. Liu, and S. Li, “Decision level fusion for pulse signal classification using multiple features,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), vol. 2, pp. 843–847, IEEE, Yantai, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  87. X. Jiang, “Doppler blood flow signal analysis meets traditional Chinese pulse diagnosis,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), vol. 2, pp. 864–868, IEEE, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  88. D. Zhang, K. Wang, X. Wu, B. Huang, and N. Li, “Hilbert-huang transform based Doppler blood flow signals analysis,” in Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (BMEI '09), pp. 1–5, IEEE, 2009.
  89. Y. Chen, D. Zhang, D. Zhang, and D. Zhang, “Pattern classification for Doppler ultrasonic wrist pulse signals,” in Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '09), pp. 1–4, IEEE, Beijing, China, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  90. D. Zhang, D. Zhang, D. Zhang, and Y. Zheng, “Wavelet based analysis of doppler ultrasonic wrist-pulse signals,” in Proceedings of the International Conference on BioMedical Engineering and Informatics (BMEI '08), vol. 2, pp. 539–543, IEEE, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  91. L. Liu, N. Li, W. Zuo, D. Zhang, and H. Zhang, “Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis,” in Intelligent Science and Intelligent Data Engineering, vol. 7751 of Lecture Notes in Computer Science, pp. 475–482, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  92. D.-Y. Zhang, W.-M. Zuo, D. Zhang, H.-Z. Zhang, and N.-M. Li, “Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features,” Journal of Biomedical Science and Engineering, vol. 3, no. 4, pp. 361–366, 2010. View at Publisher · View at Google Scholar
  93. J. Zhang, R. Wang, S. Lu et al., “EasiCPRS: design and implementation of a portable Chinese pulse-wave retrieval system,” in Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys '11), pp. 149–161, ACM, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  94. Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Wrist pulse signal diagnosis using modified Gaussian models and fuzzy C-Means classification,” Medical Engineering & Physics, vol. 31, no. 10, pp. 1283–1289, 2009. View at Publisher · View at Google Scholar · View at Scopus
  95. L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 4, pp. 598–606, 2012. View at Publisher · View at Google Scholar · View at Scopus
  96. X. Hu, H. Zhu, J. Xu, D. Xu, and J. Dong, “Wrist pulse signals analysis based on deep convolutional neural networks,” in Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–7, IEEE, May 2014. View at Publisher · View at Google Scholar
  97. H. Zhao, W.-H. Xiong, X. Zhao, L.-M. Wang, and J.-X. Chen, “Development and evaluation of a Traditional Chinese Medicine syndrome questionnaire for measuring sub-optimal health status in China,” Journal of Traditional Chinese Medicine, vol. 32, no. 2, pp. 129–136, 2012. View at Publisher · View at Google Scholar · View at Scopus
  98. 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, pp. 9–20, 2012. View at Publisher · View at Google Scholar
  99. X. Liu, P. Lu, X. Zuo, Y. Gao, Y. Yang, and J. Chen, “A new method of modeling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine,” in Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI '11), vol. 3, pp. 1631–1634, IEEE, Shanghai, China, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  100. G.-P. Liu, G.-Z. Li, Y.-L. Wang, and Y.-Q. Wang, “Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning,” BMC Complementary and Alternative Medicine, vol. 10, no. 1, article 37, 2010. View at Publisher · View at Google Scholar · View at Scopus
  101. H. Shao, G. Li, G. Liu, and Y. Wang, “Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine,” Science China. Information Sciences, vol. 56, no. 5, Article ID 052118, 13 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  102. G.-P. Liu, J.-J. Yan, Y.-Q. Wang et al., “Deep learning based syndrome diagnosis of chronic gastritis,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 938350, 8 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  103. X.-B. Yang, Z.-H. Liang, G. Zhang, Y.-J. Luo, and J. Yin, “A classification algorithm for TCM syndromes based on P-SVM,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '05), vol. 6, pp. 3692–3697, IEEE, Guangzhou, China, August 2005. View at Scopus
  104. C. Xia, D. Feng, Y. Wang et al., “Classification research on syndromes of TCM based on SVM,” in Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (BMEI '09), pp. 1–4, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  105. T. Liu, C. Xia, Y. Wang, and J. Xu, “Classifying syndromes in traditional Chinese medicine based on isomap-svm,” in Proceedings of the 5th IEEE International Conference on Biomedical Engineering and Informatics (BMEI '12), pp. 464–468, 2012.
  106. Z. Sun, G. Xi, and J. Yi, “Differentiation of syndromes with SVM,” in Advances in Neural Networks—ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28–June 1, 2006, Proceedings, Part III, vol. 3973 of Lecture Notes in Computer Science, pp. 786–791, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  107. Y. Wang, Z. Yu, Y. Jiang, Y. Liu, L. Chen, and Y. Liu, “A framework and its empirical study of automatic diagnosis of traditional Chinese medicine utilizing raw free-text clinical records,” Journal of Biomedical Informatics, vol. 45, no. 2, pp. 210–223, 2012. View at Publisher · View at Google Scholar · View at Scopus
  108. Y. Wang, L. Ma, and P. Liu, “Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine,” Computer Methods and Programs in Biomedicine, vol. 95, no. 3, pp. 249–257, 2009. View at Publisher · View at Google Scholar · View at Scopus
  109. G.-Z. Li, S. Yan, M. You, S. Sun, and A. Ou, “Intelligent ZHENG classification of hypertension depending on ML-kNN and information fusion,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 837245, 5 pages, 2012. View at Publisher · View at Google Scholar
  110. Z. X. Xu, J. Xu, J. J. Yan et al., “Analysis of the diagnostic consistency of Chinese medicine specialists in cardiovascular disease cases and syndrome identification based on the relevant feature for each label learning method,” Chinese Journal of Integrative Medicine, vol. 21, no. 3, pp. 217–222, 2015. View at Publisher · View at Google Scholar
  111. H. Wang, X. Liu, B. Lv, F. Yang, and Y. Hong, “Reliable multi-label learning via conformal predictor and random forest for syndrome differentiation of chronic fatigue in traditional Chinese medicine,” PLoS ONE, vol. 9, no. 6, Article ID e99565, 2014. View at Publisher · View at Google Scholar
  112. H. Wang and J. Wang, “A quantitative diagnostic method based on Bayesian networks in traditional Chinese medicine,” in Neural Information Processing, pp. 176–183, Springer, 2006. View at Google Scholar
  113. X. Wang, H. Qu, P. Liu, and Y. Cheng, “A self-learning expert system for diagnosis in traditional Chinese medicine,” Expert Systems with Applications, vol. 26, no. 4, pp. 557–566, 2004. View at Publisher · View at Google Scholar · View at Scopus
  114. N. Chu, M. Zhou, Y. Zhao, Z. Che, and L. Ma, “An intelligent diagnosis method for Chronis hepatitis B in TCM,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '13), pp. 20–22, IEEE, Shanghai, China, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  115. Y. Wang, L. Ma, X. Liao, and P. Liu, “Decision tree method to extract syndrome differentiation rules of posthepatitic cirrhosis in traditional Chinese medicine,” in Proceedings of the IEEE International Symposium on IT in Medicine and Education (ITME '08), pp. 744–748, IEEE, Xiamen, China, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  116. M. Shi and C. Zhou, “An approach to syndrome differentiation in traditional chinese medicine based on neural network,” in Proceedings of the 3rd International Conference on Natural Computation (ICNC '07), vol. 1, pp. 376–380, IEEE, Haikou, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  117. H. Zhang, Y. Wang, L. Wang, Y. Lin, and P. Liu, “A hierarchical diagnosis model for syndrome prediction in TCM of post-hepatitic cirrhosis,” International Journal of Integrative Medicine, vol. 1, no. 24, pp. 1–7, 2013. View at Publisher · View at Google Scholar
  118. F. Guo, Y. Lin, S. Li, and Y. Dai, “Interval-valued cloud model based personal sub-health status diagnosing prototype system on TCM syndrome data,” in Proceedings of the 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC '12), pp. 803–810, IEEE, Fukuoka, Japan, September 2012. View at Publisher · View at Google Scholar
  119. B. Wang, M.-W. Zhang, B. Zhang, and W.-J. Wei, “Data mining application to syndrome differentiation in traditional Chinese medicine,” in Proceedings of the 7th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT '06), pp. 128–131, IEEE, Taipei, Taiwan, December 2006. View at Publisher · View at Google Scholar · View at Scopus
  120. N. L. Zhang, S. Yuan, T. Chen, and Y. Wang, “Latent tree models and diagnosis in traditional Chinese medicine,” Artificial Intelligence in Medicine, vol. 42, no. 3, pp. 229–245, 2008. View at Publisher · View at Google Scholar · View at Scopus
  121. Y.-F. Zhao, L.-Y. He, B.-Y. Liu et al., “Syndrome classification based on manifold ranking for viral hepatitis,” Chinese Journal of Integrative Medicine, vol. 20, no. 5, pp. 394–399, 2014. View at Publisher · View at Google Scholar · View at Scopus
  122. J. Wang, Q. He, K.-W. Yao, W. Rong, Y. Xing, and Z. Yue, “Support vector machine (SVM) and traditional Chinese medicine: syndrome factors based an SVM from coronary heart disease treated by prominent traditional Chinese medicine doctors,” in Proceedings of the 5th International Conference on Natural Computation (ICNC '09), pp. 176–180, IEEE, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  123. M. Wang, Z. Geng, M. Wang, F. Chen, W. Ding, and M. Liu, “Combination of network construction and cluster analysis and its application to traditional Chinese medicine,” in Advances in Neural Networks—ISNN 2006, vol. 3973 of Lecture Notes in Computer Science, pp. 777–785, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  124. K. Deng, D. Liu, S. Gao, and Z. Geng, “Structural learning of graphical models and its applications to traditional Chinese medicine,” in Fuzzy Systems and Knowledge Discovery, pp. 362–367, Springer, 2005. View at Google Scholar
  125. Q. He, J. Wang, Y. Zhang, Y. Tang, and Y. Zhang, “Cluster analysis on symptoms and signs of traditional Chinese medicine in 815 patients with unstable angina,” in Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '09), vol. 1, pp. 435–439, IEEE, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  126. N. L. Zhang, S. Yuan, T. Chen, and Y. Wang, “Statistical validation of traditional Chinese medicine theories,” The Journal of Alternative and Complementary Medicine, vol. 14, no. 5, pp. 583–587, 2008. View at Publisher · View at Google Scholar · View at Scopus
  127. J.-F. Yan, Y.-Y. Peng, and W.-F. Zhu, “Experimental study of syndrome elements based on the rough set theory,” in Proceedings of the 2nd International Conference on Information and Computing Science (ICIC '09), vol. 1, pp. 39–41, IEEE, Manchester, UK, May 2009. View at Publisher · View at Google Scholar
  128. Z. Wu, X. Zhou, B. Liu, and J. Chen, “Text mining for finding functional community of related genes using tcm knowledge,” in Knowledge Discovery in Databases: PKDD 2004, pp. 459–470, Springer, 2004. View at Google Scholar
  129. X. Zhang, X. Zhou, H. Huang, S. Chen, and B. Liu, “A hierarchical symptom-herb topic model for analyzing traditional Chinese medicine clinical diabetic data,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), vol. 6, pp. 2246–2249, IEEE, Yantai, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  130. Z. Liang, G. Zhang, S. Xu et al., “A kernel-decision tree based algorithm for outcome prediction on acupuncture for neck pain: a new method for interim analysis,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '11), pp. 760–764, IEEE, Atlanta, Ga, USA, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  131. S. Qiao, C. Tang, H. Jin, J. Peng, D. Davis, and N. Han, “KISTCM: knowledge discovery system for traditional Chinese medicine,” Applied Intelligence, vol. 32, no. 3, pp. 346–363, 2010. View at Publisher · View at Google Scholar · View at Scopus
  132. X.-P. Zhang, X.-Z. Zhou, H.-K. Huang, Q. Feng, S.-B. Chen, and B.-Y. Liu, “Topic model for chinese medicine diagnosis and prescription regularities analysis: case on diabetes,” Chinese Journal of Integrative Medicine, vol. 17, no. 4, pp. 307–313, 2011. View at Publisher · View at Google Scholar · View at Scopus
  133. Y. Wang, Y. Liu, Z. Yu, L. Chen, and Y. Jiang, “A preliminary work on symptom name recognition from free-text clinical records of traditional chinese medicine using conditional random fields and reasonable features,” in Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, pp. 223–230, Association for Computational Linguistics, 2012.
  134. Y.-C. Fang, H.-C. Huang, H.-H. Chen, and H.-F. Juan, “TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining,” BMC Complementary and Alternative Medicine, vol. 8, article 58, 2008. View at Publisher · View at Google Scholar · View at Scopus
  135. M. Yang, J. Poon, S. Wang et al., “Application of genetic algorithm for discovery of core effective formulae in TCM clinical data,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 971272, 16 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  136. X. Tang, N. Zhang, and Z. Wang, “Exploration of TCM masters knowledge mining,” Journal of Systems Science and Complexity, vol. 21, no. 1, pp. 34–45, 2008. View at Publisher · View at Google Scholar · View at Scopus
  137. M.-J. Huang and M.-Y. Chen, “Integrated design of the intelligent web-based Chinese Medical Diagnostic System (CMDS)—systematic development for digestive health,” Expert Systems with Applications, vol. 32, no. 2, pp. 658–673, 2007. View at Publisher · View at Google Scholar · View at Scopus
  138. B. Pang, D. Zhang, and K. Wang, “Tongue image analysis for appendicitis diagnosis,” Information Sciences, vol. 175, no. 3, pp. 160–176, 2005. View at Publisher · View at Google Scholar · View at Scopus
  139. L. Yang, R. Wang, G. Wang, and W. Zhang, Human Pulse Patterns Recognition Using Improved Echo State Network, Sciencepaper Online, Beijing, China, 2011.
  140. C.-C. Chiu, B. Y. Liau, S. J. Yeh, and C. L. Hsu, “Artificial neural network classification of arterial pulse waveforms in cardiovascular diseases,” in Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering (Biomed '08), pp. 129–132, mys, June 2008. View at Scopus