Traditional Chinese Medicine Aided Diagnosis and Treatment System for Rheumatoid Arthritis Based on Artificial Intelligence
SUN Mingjun1, ZHANG Dan2, ZHENG Mingzhi3, MEI Shuhuan2
1. Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology, Beijing 100191 2. Technical Department, Nanjing Research Institute of Next-Generation Artificial Intelligence, Nanjing 210046 3. Hangzhou Research Center of Artificial Intelligence, China Academy of Information and Communications Technology, Hangzhou 311121
Abstract Rheumatoid arthritis(RA) is a widespread, chronic and refractory systemic immune rheumatism. Traditional Chinese medicine(TCM) presents the advantages of less side effects and low price. However, the spread of RA TCM diagnosis and treatment scheme with curative effect advantages is limited due to the lack of experienced TCM practitioners, especially in primary medical institutions. In this paper, a traditional Chinese medicine aided diagnosis and treatment system for RA based on artificial intelligence is proposed. RA and pattern of syndrome in RA can be determined after learning patient medical records and medical imaging of joints, and then TCM prescription is recommended intelligently according to the pattern. Next, the information is exploited to assist doctors in diagnosis. Based on RA TCM knowledge, the knowledge graph is built. It provides doctors with knowledge guidance in the process of diagnosis and treatment. The system can assist less experienced doctors in making treatment decisions, improving the treatment level of RA, and studying and promoting RA treatment.
Fund:National Key R&D Program of China(No.2018YFC1705504)
Corresponding Authors:
SUN Mingjun, master, senior engineer. Her research inte-rests include artificial intelligence and multimedia.
About author:: ZHANG Dan, master, engineer. Her research interests include natural language processing. ZHENG Mingzhi, bachelor, engineer. His research interests include natural language processing and wise medical. MEI Shuhuan, master, engineer. His research interests include image retrieval and image recognition.)
SUN Mingjun,ZHANG Dan,ZHENG Mingzhi等. Traditional Chinese Medicine Aided Diagnosis and Treatment System for Rheumatoid Arthritis Based on Artificial Intelligence[J]. , 2021, 34(4): 343-352.
[1] 顾冬梅,蒋 恬,刘西强.类风湿性关节炎中医药治疗研究进展.新中医, 2013, 45(4): 139-142. (GU D M, JIANG T, LIU X Q. Advances in the Treatment of Rheumatoid Arthritis with Traditional Chinese Medicine. Journal of New Chinese Medicine, 2013, 45(4): 139-142.) [2] 蔡 雄,周 华,徐宏喜,等.类风湿性关节炎的中医临床治疗研究概况.中华中医药杂志, 2005, 20(5): 309-311. (CAI X, ZHOU H, XU H X, et al. Overview of Clinical Treatment of Rheumatoid Arthritis in Traditional Chinese Medicine. China Journal of Traditional Chinese Medicine and Pharmacy, 2005, 20(5): 309-311.) [3] 俞益洲,石德君,马杰超,等.人工智能在医学影像分析中的应用进展.中国医学影像技术, 2019, 35(12): 1808-1812. (YU Y Z, SHI D J, MA J C, et al. Advances in the Application of Artificial Intelligence in Medical Imaging Analysis. Chinese Journal of Medical Imaging Technology, 2019, 35(12): 1808-1812.) [4] 余帅帅,叶云程,曾碧新.自身免疫疾病辅助诊断专家系统.计算机应用与软件, 2011, 28(1): 89-91. (YU S S, YE Y C, ZENG B X. An Expert System for Assisted Diagnosis for Autoimmune Diseases. Computer Applications and Software, 2011, 28(1): 89-91.) [5] 浦东旭.基于病历文本语义分析的智能肝病辅助诊疗系统研究.硕士学位论文.杭州:浙江大学, 2018. (PU D X. Research of Intelligent Hepatopathy Auxiliary Diagnosis System Based on Text Semantic Analysis of Electronic Medical Records. Master Dissertation. Hangzhou, China: Zhejiang University, 2018.) [6] 陈 悦,刘泽渊,陈 劲,等.科学知识图谱的发展历程.科学学研究, 2008, 26(3): 449-460. (CHEN Y, LIU Z Y, CHEN J, et al. History and Theory of Ma-pping Knowledge Domains. Studies in Science of Science, 2008, 26(3): 449-460.) [7] 贾李蓉,刘 静,于 彤,等.中医药知识图谱构建.医学信息学杂志, 2015, 36(8): 51-53, 59. (JIA L R, LIU J, YU T, et al. Construction of Traditional Chinese Medicine Knowledge Graph. Journal of Medical Informatics, 2015, 36(8): 51-53, 59.) [8] 杨锦锋,于秋滨,关 毅,等.电子病历命名实体识别和实体关系抽取研究综述.自动化学报, 2014, 40(8): 1537-1562. (YANG J F, YU Q B, GUAN Y, et al. An Overview of Research on Electronic Medical Record Oriented Named Entity Recognition and Entity Relation Extraction. Acta Automatica Sinica, 2014, 40(8): 1537-1562.) [9] LI W, YANG Z. Exploration on Generating Traditional Chinese Medi-cine Prescriptions from Symptoms with an End-to-End Approach // Proc of the CCF International Conference on Natural Language Processing and Chinese Computing. Berlin, Germany: Springer, 2019: 486-498. [10] 赵移畛,尚东挺.中医电子病历系统应具备的中医特点.中医药管理杂志, 2007, 15(12): 894-896. (ZHAO Y Z, SHAO D T. Characteristics of TCM That Should Be Possessed by TCM Electronic Medical Record System. Chinese Journal of Management in Chinese Medicine, 2007, 15(12): 894-896.) [11] PAN S J, YANG Q. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359. [12] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding // Proc of NAACL-HLT 2019. Stroudsburg, USA: ACL, 2018: 4171-4186. [13] LAFFERTY J, MCCALLUM A, PEREIRA F C N. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data // Proc of the 18th International Conference on Machine Learning. New York, USA: ACM, 2001: 282-289. [14] ARNETT F C, EDWORTHY S M, BLOCH D A, et al. The Ame-rican Rheumatism Association 1987 Revised Criteria for the Classification of Rheumatoid Arthritis. Arthritis and Rheumatism, 1988, 31(3): 315-324. [15] ALETAHA D, NEOGI T, SILMAN A J, et al. 2010 Rheumatoid Arthritis Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative. Annals of the Rheumatic Diseases, 2010, 69(9): 1580-1588. [16] 中华医学会风湿病学分会.类风湿关节炎诊断及治疗指南.中华风湿病学杂志, 2010, 14(4): 265-270. (Rheumatology Branch of Chinese Medical Association. Guidelines for the Diagnosis and Treatment of Rheumatoid Arthritis. Chinese Journal of Rheumatology, 2010, 14(4): 265-270.) [17] HOCHREITER S, SCHMIDHUBER J. Long Short-Term Memory. Neural Computation, 1997, 9(8): 1735-1780. [18] 于 彤,刘 静,贾李蓉,等.大型中医药知识图谱构建研究.中国数字医学, 2015, 10(3): 80-82. (YU T, LIU J, JIA L R, et al. Research on the Construction of Big Knowledge Graph for Traditional Chinese Medicine. China Digital Medicine, 2015, 10(3): 80-82.)
2021, Vol. 34 
