How to organise a datathon for bridging between data science and healthcare? Insights from the Technion-Rambam machine learning in healthcare datathon event.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Jonathan Sobel, Ronit Almog, Leo Celi, Michal Yablowitz, Danny Eytan, Joachim Behar
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Abstract

© Author(s) (or their employer(s)) 2023. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. INTRODUCTION A datathon is a timeconstrained informationbased competition involving data science applied to one or more challenges. Datathons and hackathons differ in their focus, with datathons prioritising data analysis and modelling, while hackathons concentrate on building prototypes. Furthermore, hackathons can encompass a broad range of topics, spanning from software development to hardware design, whereas datathons are more narrowly focused on data analysis. Inperson datathons offer the unique opportunity to learn alongside a community of fellow students and researchers, as well as to directly interact with clinicians and medical professionals. This is in contrast to Kaggle like competitions, which are often selflearning experiences.

Abstract Image

如何组织一场数据马拉松,架起数据科学与医疗保健之间的桥梁?来自医疗保健数据马拉松活动中的Technion-Rambam机器学习的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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