推动医疗大数据应用落地的跨学科Datathon模式探索

A cross-discivlinarv collaborative ＂Datathon＂ model to promote the application of medical big data

医疗实践活动产生海量的数据，对这些数据的整合、分析和利用需要跨学科合作，需要新的模式和方法，以推动医疗大数据应用和人工智能研究的落地。Datathon是一项新颖的数据实践活动，通过组建理-工-医结合的跨学科团队，以短期高强度的项目竞赛形式，基于＂真实世界＂的数据来探索解决临床研究问题。本文介绍了Datathon活动的背景起源、组织模式和相关实践。Datathon活动为国内医疗领域的跨学科合作提供了创新协作的新模式，为如何开展医疗大数据研究提供了新思路，有利于不同学科间优势互补，凝聚多方力量一起探索解答临床实践中遇到的问题，并推动医疗大数据实践应用落地。

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.