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Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

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收录情况: ◇ SCIE ◇ EI

机构: [1]Department of Physiology and Pathophysiology, Capital Medical University, Beijing 100069, China [2]State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China [3]School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China [4]International Open Benchmarking Council, Dover, DE 19901, USA [5]Nanchang First Hospital, Nanchang 330008, China [6]National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China [7]Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, China [8]Benchmarking Research Center, Beijing Academy of Frontier Sciences and Technology, Beijing 100080, China
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关键词: Big medical data quantified self disease classification disease diagnosis drug discovery publicly available data benchmarks algorithms systems multi-disciplinary collaboration

摘要:
Big medical data pose great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists, and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of the state-of-the-practice and state-of-the-art systems handling big medical data currently or in the future? Finally, but not least, are we, life scientists, clinicians, computer scientists, and engineers, ready for working together? We believe that answering the above-mentioned issues will help define and shape the landscape of big medical data.

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出版当年[2018]版:
大类 | 2 区 工程技术
小类 | 2 区 计算机:信息系统 3 区 工程:电子与电气 3 区 电信学
最新[2023]版:
大类 | 3 区 计算机科学
小类 | 3 区 工程:电子与电气 4 区 计算机:信息系统 4 区 电信学
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出版当年[2017]版:
Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Q1 TELECOMMUNICATIONS Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
最新[2023]版:
Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Q2 TELECOMMUNICATIONS

影响因子: 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

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第一作者机构: [1]Department of Physiology and Pathophysiology, Capital Medical University, Beijing 100069, China
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通讯机构: [1]Department of Physiology and Pathophysiology, Capital Medical University, Beijing 100069, China
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