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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
[A cross-disciplinary collaborative "Datathon" model to promote the application of medical big data]
Yuan Zhang1, Peiyao Li, Yuzhuo Zhao
1Chinese 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,
Objective:
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.
