Parallel Gout: An ACP-Based System Framework for Gout Diagnosis and Treatment
WANG Fei-Yue1,3, LI Changgui4, GUO Yuanyuan1,5, WANG Jing1,3, WANG Xiao2,3, QIU Tianyu1, Meng Xiangbing1,3, SHI Xiaobo1
1.Parallel Healthcare Technology Innovation Center, Institute of Smart Healthcare Systems,Qingdao Academy of Intelligent Industries, Qingdao 266109 2.Parallel Workshop, Qingdao Academy of Intelligent Industries, Qingdao 266109 3.The State Key Laboratory for Management and Control for Complex Systems, Institute of Automation,Chinese Academy of Sciences, Beijing 100190 4.Shandong Gout Clinical Medical Center, The Affiliated Hospital of Qingdao University, Qingdao266003 5.Department of Industrial Engineering, Tsinghua University, Beijing 100084
Abstract：In order to improve the accuracy and the efficiency of diagnosis and treatment of Gout in complicated situation and break through the gap between doctor′s profession, an artificial societies, computational experiments, and parallel execution(ACP) based parallel gout diagnosis and treatment framework is presented, named Parallel Gout. Parallel Gout could construct an artificial gout diagnosis and treatment system to represent and simulate the real procedure of medical diagnosis and treatment system. It can train and evaluate the various models of diagnosis and treatment of gout by Computational Experiments, and to manage and control the real medical system by Parallel Execution, to achieve the automatic and intelligent diagnosis and treatment procedure for gout. Parallel Gout can help doctors to reduce the likelihood of misdiagnosis and therapeutic mistakes, and increase the efficiency of medical procedure as well improve the professionality of doctors. Parallel Gout can also help the patients to manage the chronic diseases and keep healthy. The application of Parallel Gout in the diagnosis and treatment of gout is of great practical significance. This is an effective approach to motivate the traditional medical model to become intelligent and parallel, and contribute to a higher level of national health.
王飞跃，李长贵，国元元，王静，王晓，邱天雨，孟祥冰，施小博. 平行高特:基于ACP的平行痛风诊疗系统框架*[J]. 模式识别与人工智能, 2017, 30(12): 1057-1068.
WANG Fei-Yue, LI Changgui, GUO Yuanyuan, WANG Jing, WANG Xiao, QIU Tianyu, Meng Xiangbing, SHI Xiaobo. Parallel Gout: An ACP-Based System Framework for Gout Diagnosis and Treatment. , 2017, 30(12): 1057-1068.
[1] 王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策, 2004, 19(5): 485-489, 514. (WANG F Y. Parallel System Methods for Management and Control of Complex Systems. Control and Decision, 2004, 19(5): 485-489, 514.) [2] 王飞跃.人工社会、计算实验、平行系统——关于复杂社会经济系统计算研究的讨论.复杂系统与复杂性科学, 2004, 1(4): 25-35. (WANG F Y. Artificial Societies, Computational Experiments and Parallel Systems: A Discussion on Computational Theory of Complex Social-Economic Systems. Complex Systems and Complexity Science, 2004, 1(4): 25-35.) [3] WANG F Y, WONG P K. Intelligent Systems and Technology for Integrative and Predictive Medicine: An ACP Approach. ACM Transactions on Intelligent Systems & Technology, 2013, 4(2). DOI: 10.1145/2438653.2438667. [4] 王飞跃.平行医学与平行健康:迈向智能医学和智慧健康的智能科技.智能产业发展战略报告.青岛:青岛智能产业技术研究院(QAII), 2014. (WANG F Y. Parallel Medicine and Parallel Health: Technologies towards Intelligent Medicine and Intelligent Health. Reports on the Development of Intelligent Industry. Qingdao, China: Qingdao Academy of Intelligent Industries (QAII), 2014.) [5] 王飞跃.平行医学.亚太痛风联盟成立大会&第七届东方痛风论坛.哈尔滨:哈尔滨医科大学附属第四医院, 2017. (WANG F Y. Parallel Medicine. Asia Pacific Gout Conference & the 7th East Gout Forum. Harbin, China: The 4th Affliated Hospital of Harbin Medical University, 2017.) [6] 王飞跃.平行控制:数据驱动的计算控制方法.自动化学报, 2013, 39(4): 293-302. (WANG F Y. Parallel Control: A Method for Data-Driven and Computational Control. Acta Automatica Sinica, 2013, 39(4): 293-302.) [7] 母义明.高尿酸血症和痛风实用诊疗手册.天津:天津科技翻译出版公司, 2014. (MU Y M. Handbook for Diagnosis and Treatment of Hyperuricemia and Gout. Tianjin, China: Tianjin Science & Technology Translation & Publishing Corp., 2014.) [8] 李长贵.实用痛风病学.北京:人民军医出版社, 2016. (LI Changgui. Practice of Gout. Beijing, China: People's Military Medical Press, 2016.) [9] FERRUCCI D A. Introduction to “This is Watson”. IBM Journal of Research and Development, 2012, 56(3/4): 1:1-1:15. [10] FAN J, KALYANPUR A, GONDEK D C, et al. Automatic Knowledge Extraction from Documents. IBM Journal of Research & Development, 2012, 56(3): 290-299. [11] WANG F Y. The Emergence of Intelligent Enterprises: From CPS to CPSS. IEEE Intelligent Systems, 2010, 25(4): 85-88. [12] 王坤峰,苟 超,段艳杰,等.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(2): 321-332. (WANG K F, GOU C, DUAN Y J, et al. Generative Adversarial Networks: The State of the Art and Beyond. Acta Automatica Sinica, 2017, 43(3): 321-332.) [13] 李 力,林懿伦,曹东璞,等.平行学习——机器学习的一个新型理论框架.自动化学报, 2017, 43(1): 1-8. (LI L, LIN Y L, CAO D P, et al. Parallel Learning-A New Framework for Machine Learning. Acta Automatica Sinica, 2017, 43(1): 1-8.) [14] 王飞跃.计算实验方法与复杂系统行为分析和决策评估.系统仿真学报, 2004, 16(5): 893-897. (WANG F Y. Computational Experiments for Behavior Analysis and Decision Evaluation of Complex Systems. Journal of System Simulation, 2004, 16(5): 893-897.) [15] 王飞跃.软件定义的系统与知识自动化:从牛顿到默顿的平行升华.自动化学报, 2015, 41(1): 1-8. (WANG F Y. Software-Defined Systems and Knowledge Automation: A Parallel Paradigm Shift from Newton to Merton. Acta Automatica Sinica, 2015, 41(1): 1-8.) [16] 王飞跃.指控5.0:平行时代的智能指挥与控制体系.指挥与控制学报,2015, 1(1): 107-120. (WANG F Y. CC5.0: Intelligent Command and Control Systems in the Parallel Age. Journal of Command and Control, 2015, 1(1): 107-120.) [17] 王飞跃.机器人的未来发展:从工业自动化到知识自动化.科技导报, 2015, 33(21): 39-44. (WANG F Y. On Future Development of Robotics: From Industrial Automation to Knowledge Automation. Science & Technology Review, 2015, 33(21):39-44.) [18] 王飞跃,张 梅,孟祥冰,等.平行手术:基于ACP 的智能手术计算方法.模式识别与人工智能, 2017, 30(11): 961-970. (WANG F Y, ZHANG M, MENG X B, et al. Parallel Surgery: An ACP-Based Approach for Intelligent Operations. Pattern Recognition and Artificial Intelligence, 2017, 30(11): 961-970.) [19] 刘 昕,王 晓,张卫山,等.平行数据:从大数据到数据智能.模式识别与人工智能, 2017, 30(8): 673-681. (LIU X, WANG X, ZHANG W S, et al. Parallel Data: From Big Data to Data Intelligence. Pattern Recognition and Artificial Intelligence, 2017, 30(8): 673-681.) [20] 王坤峰,鲁 越,王雨桐,等.平行图像:图像生成的一个新型理论框架.模式识别与人工智能, 2017, 30(7): 577-587. (WANG K F, LU Y, WANG Y T, et al. Parallel Imaging: A New Theoretical Framework for Image Generation. Pattern Recognition and Artificial Intelligence, 2017, 30(7): 577-587.) [21] 王飞跃,邱晓刚,曾大军,等.基于平行系统的非常规突发事件计算实验平台研究.复杂系统与复杂性科学, 2010, 7(4): 1-10. (WANG F Y, QIU X G, ZENG D J, et al. A Computational Experimental Platform for Emergency Response Based on Parallel Systems. Complex Systems and Complexity Science, 2010, 7(4): 1-10.) [22] 王坤峰,苟 超,王飞跃.平行视觉:基于ACP 的智能视觉计算方法.自动化学报, 2016, 42(10): 1490-1500. (WANG K F, GOU C, WANG F Y. Parallel Vision: An ACP-Based Approach to Intelligent Vision Computing. Acta Automatica Sinica, 2016, 42(10): 1490-1500.) [23] 王飞跃.基于社会计算和平行系统的动态网民群体研究.上海理工大学学报, 2011, 33(1): 8-17. (WANG F Y. Study on Cyber-Enabled Social Movement Organizations Based on Social Computing and Parallel Systems. Journal of University of Shanghai for Science and Technology, 2011, 33(1): 8-17.) [24] 王飞跃,王 晓,袁 勇,等.社会计算与计算社会:智慧社会的基础与必然.科学通报, 2015, 60(5/6): 460-469. (WANG F Y, WANG X, YUAN Y, et al. Social Computing and Computational Societies: The Foundation and Consequence of Smart Societies. Chinese Science Bulletin, 2015, 60(5/6): 460-469.) [25] 袁 勇,王飞跃.平行区块链:概念、方法与内涵解析.自动化学报, 2017, 43(10): 1703-1712. (YUAN Y, WANG F Y. Parallel Blockchain: Concept, Methods and Issues. Acta Automatica Sinica, 2017, 43(10): 1703-1712.) [26] 白天翔,王 帅,沈 震,等.平行机器人与平行无人系统:框架、结构、过程、平台及其应用.自动化学报, 2017, 43(2):161-175. (BAI T X, WANG S, SHEN Z, et al. Parallel Robotics and Parallel Unmanned Systems: Framework, Structure, Process, Platform and Applications. Acta Automatica Sinica, 2017, 43(2): 161-175.) [27] WANG F Y, ZHENG N N, CAO D P, et al. Parallel Driving in CPSS: A Unified Approach for Transport Automation and Vehicle Intelligence. IEEE/CAA Journal of Automatica Sinica, 2017, 4(4): 577-587. [28] WANG F Y, ZHANG J, WEI Q L, et al. PDP: Parallel Dynamic Programming. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1): 1-5. [29] ZHANG N, WANG F Y, ZHU F H, et al. DynaCAS: Computational Experiments and Decision Support for ITS. IEEE Intelligent Systems, 2008, 23(6): 19-23. [30] WANG F Y. Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 630-638. [31] LI L, WEN D. Parallel Systems for Traffic Control: A Rethinking. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4): 1179-1182. [32] WANG F Y, WANG X, LI L X, et al. Steps toward Parallel Intelligence. IEEE/CAA Journal of Automatica Sinica, 2016, 3(4): 345-348. [33] 漆桂林,高 桓,吴天星.知识图谱研究进展.情报工程, 2017, 3(1): 4-25. (QI G L, GAO H, WU T X. The Research Advances of Knowledge Graph. Technology Intelligence Engineering, 2017, 3(1): 4-25.)
2017, Vol. 30 
