Paper Info
Title
Landscape Of Big Medical Data: A Pragmatic Survey On Prioritized Tasks
Abstract
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.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2891948
IEEE ACCESS
Keywords
DocType
Volume
Big medical data, quantified self, disease classification, disease diagnosis, drug discovery, publicly available data, benchmarks, algorithms, systems, multi-disciplinary collaboration
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
66
16
Name
Order
Citations
PageRank
Zhifei Zhang11010.25
Wanling Gao229919.12
Fan Zhang35416.27
Yunyou Huang423.46
Shaopeng Dai500.34
Fanda Fan611.71
Jianfeng Zhan776762.86
Mengjia Du800.34
Silin Yin900.34
Longxin Xiong1000.34
Juan Du11138.05
Yumei Cheng1200.34
Xiexuan Zhou1300.34
Rui Ren14396.66
Lei Wang1557746.85
hainan ye1673.93