EAGER: SCH: Distributed and Adaptive Personalized Medicine

临床数据的使用可以提高医疗实践的质量。然而，目前的经验方法是根据人口平均值制定的，而个体差异往往被忽视。此外，这些方法是静态的，不能随着时间的推移以自适应的方式学习和改变。因此，医学科学还没有在所有可用数据的基础上个性化和适应性。该项目整合了医学、工程和最先进的信息技术，旨在研究分布式和自适应的个性化医疗，这些医疗可以实时地从个人的临床数据中学习。这种方法的灵感来自于在自然界中观察到的集体和适应性学习（例如，蜜蜂的蜂群行为，鸟类的飞行编队等）。该项目的成果将使针对个别患者的更智能的诊断、治疗和预防成为可能。它将通过出版物、研讨会、讲习班和大规模开放在线课程（MOOC）的开发广泛传播。临床数据的使用是当今医疗实践的核心，虽然已经开发了各种数学和计算方法，但传统方法并不针对个体患者或不断变化的临床数据的动态。受多细胞动力学研究的启发，该项目探索分布式和自适应个性化医疗，通过局部交互，从个人临床数据中实时学习。为了使这些努力成为可能并可扩展，该项目将利用微服务（参与者模型）支持的云网络基础设施来提高可访问性、适应性、互操作性、可扩展性、可扩展性和可持续性。此外，该项目的成果，包括数学框架，可以应用于其他领域，如教育、能源、电信和交通。它还将通过出版物、研讨会、讲习班和MOOC向学术界传播，将这项工作的结果整合到跨学科的生物医学信息学研究中。所有工具和文档都将在GitHub上提供，以便围绕项目形成一个可持续的社区。

The quality of medical practice can be improved by the use of clinical data. However, current empirical approaches are developed on population averages and individual differences are often ignored. Furthermore, these approaches are static and fail to learn and change over time in an adaptive fashion. As a result, the science of medicine is not yet personalized and adaptive based on all the available data. Integrating medicine, engineering, and state-of-the-art information technology, this project aims at studying distributed and adaptive personalized medicine that collectively learns from an individual's clinical data in real time. This approach is inspired by collective and adaptive learning observed in nature (for example, honeybee swarming behavior, bird flight formation, etc.). The outcome of this project will enable smarter diagnosis, treatment, and prevention tailored towards individual patients. It will be disseminated widely through publications, seminars, workshops, and MOOC (Massive Open Online Course) development. The use of clinical data is at the core of medical practice today and, while various mathematical and computational approaches have been developed, conventional approaches are not geared towards individual patients or the dynamics of constantly changing clinical data. Inspired by studies of multicellular dynamics, this project explores distributed and adaptive personalized medicine which collectively learns from an individual's clinical data in real time through localized interactions. To make these efforts possible and scalable, this project will exploit a microservice (actor model)-enabled cloud cyberinfrastructure for increased accessibility, adaptability, interoperability, extensibility, scalability and sustainability. In addition, the result of this project, including the mathematical framework, can be applied to other domains, such as education, energy, telecommunications, and transportation. It will also be disseminated to academia through publications, seminars, workshops, and a MOOC to integrate the results of this work into interdisciplinary biomedical informatics research. All tools and documentation will be made available on GitHub so that a sustainable community can be formed around the project.