Intelligent diagnosis model of traditional Chinese medicine based on active learning in big data_Chinese Journal of Evidence-Based Medicine

Authors：

REN Xue ¹ ,  GUO Yan ²

1. Jinan Municipal Hospital of Traditional Chinese Medicine, Jinan, 250012, P.R.China;
2. Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, 100091, P.R.China;

Corresponding author：

GUO Yan, Email: guoyan0314@126.com

Keywords：

Intelligent diagnosis of traditional Chinese medicine; Machine learning; Active integrated learning; Traditional Chinese medicine; Big data

DOI：

10.7507/1672-2531.201904028

Video：

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As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention in both academia and industry. Traditional Chinese medicine (TCM) is characterized by individual syndrome differentiation as well as personalized treatment with personality analysis, which makes the common law mining technology of big data and artificial intelligence appear distortion in TCM diagnosis and treatment study. This article put forward an intelligent diagnosis model of TCM, as well as its construction method. It could not only obtain personal diagnosis varying individually through active learning, but also integrate multiple machine learning models for training, so as to form a more accurate model of learning TCM. Firstly, we used big data extraction technique from different case sources to form a structured TCM database under a unified view. Then, taken a pediatric common disease pneumonia with dyspnea and cough as an example, the experimental analysis on large-scale data verified that the TCM intelligent diagnosis model based on active learning is more accurate than the pre-existing machine learning methods, which may provide a new effective machine learning model for studying TCM diagnosis and treatment.

Citation： REN Xue, GUO Yan. Intelligent diagnosis model of traditional Chinese medicine based on active learning in big data. Chinese Journal of Evidence-Based Medicine, 2019, 19(9): 1118-1123. doi: 10.7507/1672-2531.201904028 Copy

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1. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
2. Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am J Gastroenterol, 2010, 105(6): 1224-1226.
3. Hilario M, Kalousis A, Müller M, et al. Machine learning approaches to lung cancer prediction from mass spectra. Proteomics, 2003, 3(9): 1716-1719.
4. Coraddu A, Oneto L, Ghio A, et al. Machine learning for wear forecasting of naval assets for condition-based maintenance applications. Greece: International Conference on Electrical Systems for Aircraft, 2015.
5. Chavan SS, Mahajan A, Talbar SN, et al. Nonsubsampled rotated complex wavelet transform (NSRCxWT) for medical image fusion related to clinical aspects in neurocysticercosis. Comput Biol Med, 2017, 81: 64-78.
6. Jørgensen TM, Tycho A, Mogensen M, et al. Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography. Skin Res Technol, 2008, 14(3): 364-369.
7. Zangwill LM, Chan K, Bowd C, et al. Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. Invest Ophthalmol Vis Sci, 2004, 45(9): 3144-3151.
8. Thomas G, Grassi MA, Lee JR, et al. IDOCS: Intelligent distributed ontology consensus system-the use of machine learning in retinal druse phenotyping. Invest Opth Vis Sci, 2007, 48(5): 2278.
9. Gurovich Y, Hanani Y, Bar O, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med, 2019, 25(1): 60-64.
10. 张功耀. 告别中医中药. 医学与哲学(人文社会医学版), 2006, 27(7): 14-17.
11. 刘建平. 转化医学与循证医学及其与中医药疗效评价. 中国中西医结合杂志, 2011, 31(4): 444-445.
12. 赵国桢, 冯硕, 张霄潇, 等. 中医药临床实践指南: 现状和思考. 中国循证医学杂志, 2018, 18(12): 1386-1390.
13. 刘保延. 真实世界的中医临床科研范式. 中医杂志, 2013, 54(6): 451-455.
14. 陈伟. 中医药信息化建设机遇与挑战并存-解读《中医药信息化建设" 十二五”规划》. 中国信息界(e医疗), 2012, (8): 28-31.
15. Yang B, Sun JT, Wang TJ, et al. Effective multi-label active learning for text classification. ACM: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009: 917-926.
16. Chen W, Zhang X, Wang TJ, et al. Opinion-aware knowledge graph for political ideology detection. IJCAI: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017.
17. Huang WJ, Wang TJ, Chen W, et al. Phrase CTM: Correlated topic modeling on phrases within markov random fields. ACL: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 1-6.

Chinese Journal of Evidence-Based Medicine

Intelligent diagnosis model of traditional Chinese medicine based on active learning in big data

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