Zhang Yuan, Li Peiyao, Zhao Yuzhuo, Liu Tongbo, Zhang Zhengbo, Cao Desen, Li Tanshi, Leo Anthony Celi
Chinese PLA Medical School, Beijing 100853, China (Zhang Y); Department of Biomedical Engineering and Maintenance Center, Chinese PLA General Hospital, Beijing 100853, China (Li PY, Zhang ZB, Cao DS); Department of Emergency, Chinese PLA General Hospital, Beijing 100853, China (Zhao YZ, Li TS); Medical Information Center, Chinese PLA General Hospital, Beijing 100853, China (Liu TB, Zhang ZB); Department of Computer Application and Management, Chinese PLA General Hospital, Beijing 100853, China (Liu TB); Department of Medical Device R and D and Evaluation Center, Chinese PLA General Hospital, Beijing 100853, China (Zhang ZB, Cao DS); Laboratory of Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 01239, United States (Celi LA); Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 01238, United States (Celi LA). Corresponding author: Zhang Zhengbo, Email:
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2018 Jun;30(6):606-608. doi: 10.3760/cma.j.issn.2095-4352.2018.06.021.
Medical practice generates and stores immense amounts of clinical process data, while integrating and utilization of these data requires interdisciplinary cooperation together with novel models and methods to further promote applications of medical big data and research of artificial intelligence. A "Datathon" model is a novel event of data analysis and is typically organized as intense, short-duration, competitions in which participants with various knowledge and skills cooperate to address clinical questions based on "real world" data. This article introduces the origin of Datathon, organization of the events and relevant practice. The Datathon approach provides innovative solutions to promote cross-disciplinary collaboration and new methods for conducting research of big data in healthcare. It also offers insight into teaming up multi-expertise experts to investigate relevant clinical questions and further accelerate the application of medical big data.
医疗实践产生并存储了海量的临床过程数据，而这些数据的整合与利用需要跨学科合作以及新颖的模型和方法，以进一步推动医疗大数据的应用和人工智能研究。“数据马拉松”模式是一种新型的数据分析活动，通常组织为高强度、短时长的竞赛，让具备各种知识和技能的参与者基于“真实世界”数据合作解决临床问题。本文介绍了“数据马拉松”的起源、活动组织及相关实践。“数据马拉松”方法为促进跨学科合作提供了创新解决方案，也为开展医疗保健大数据研究提供了新方法。它还为汇聚多领域专家调查相关临床问题、进一步加速医疗大数据的应用提供了思路。
