Intelligent systems and technology for integrative and predictive medicine

Abstract

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 the ACP-based approach, that combines artificial (societies), computational (experiments), and parallel (execution) methods in intelligent systems and technology for integrative and predictive medicine, or more generally, 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, and 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 a personal, proactive, and precise nature, including rational combinatorial treatments, adaptive therapeutics, and patient-oriented disease management.