Wang Fei-Yue, Wong Pak Kin
The State Key Laboratory of Management and Control for Complex Systems, Chinese Academy of Sciences, Beijing, China ; The Research Center for Computational Experiments and Parallel Systems, The National University of Defense Technology, Changsha, Hunan, China.
Systematic Bioengineering Laboratory, The University of Arizona, Tucson, Arizona, USA.
ACM Trans Intell Syst Technol. 2013 Mar 1;4(2):32.
One of the principal goals in medicine is to determine and implement the best treatment for patients through fastidious estimation of the effects and benefits of therapeutic procedures. The inherent complexities of physiological and pathological networks that span across orders of magnitude in time and length scales, however, represent fundamental hurdles in determining effective treatments for patients. Here we argue for a new approach, called ACP-based approach that combines methods in intelligent systems and technology for integrative and predictive medicine, or more general, precision medicine and smart health management. The advent of artificial societies that collect the clinically relevant information in prognostics and therapeutics provides a promising platform for organizing and experimenting complex physiological systems toward integrative medicine. The ability of computational experiments to analyze distinct, interactive systems such as the host mechanisms, pathological pathways, therapeutic strategies as well as other factors using the artificial systems will enable control and management through parallel execution of real and arficial systems concurrently within the integrative medicine context. The development of this framework in integrative medicine fueled by close collaborations between physicians, engineers, and scientists will result in preventive and predictive practices of personal, proactive, and precision nature, including rational combinatorial treatments, adaptive therapeutics, and patient-oriented disease management.
医学的主要目标之一是通过对治疗程序的效果和益处进行细致评估,来确定并实施对患者最佳的治疗方案。然而,生理和病理网络在时间和长度尺度上跨越多个数量级,其内在的复杂性是为患者确定有效治疗方法的根本障碍。在此,我们倡导一种新方法,即基于ACP的方法,该方法结合了智能系统和技术中的方法,用于整合性和预测性医学,或者更广泛地说,用于精准医学和智能健康管理。在预后和治疗中收集临床相关信息的人工社会的出现,为朝着整合医学方向组织和试验复杂生理系统提供了一个有前景的平台。计算实验利用人工系统分析不同的交互系统(如宿主机制、病理途径、治疗策略以及其他因素)的能力,将能够在整合医学背景下通过同时并行执行真实系统和人工系统来实现控制和管理。由医生、工程师和科学家之间的紧密合作推动的这一整合医学框架的发展,将带来具有个性化、主动式和精准性的预防和预测实践,包括合理的联合治疗、适应性治疗以及以患者为导向的疾病管理。