Crowdsourcing in biomedicine: challenges and opportunities

被引:62
作者
Khare, Ritu [1 ]
Good, Benjamin M. [2 ]
Leaman, Robert [3 ]
Su, Andrew I. [2 ]
Lu, Zhiyong [4 ]
机构
[1] Childrens Hosp Philadelphia, Dept Biomed & Hlth Informat, Philadelphia, PA USA
[2] Scripps Res Inst, Dept Mol & Expt Med, San Diego, CA USA
[3] NIH, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA
[4] NIH, Natl Ctr Biotechnol Informat, Biomed Text Min Grp, Bethesda, MD 20894 USA
基金
美国国家卫生研究院;
关键词
Amazon Mechanical Turk; big data mining; biomedicine; community challenges; crowdsourcing; games; NORMALIZATION; RECOGNITION; TRENDS; CANCER; TERMS; TEXT; MESH; TOOL;
D O I
10.1093/bib/bbv021
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The use of crowdsourcing to solve important but complex problems in biomedical and clinical sciences is growing and encompasses a wide variety of approaches. The crowd is diverse and includes online marketplace workers, health information seekers, science enthusiasts and domain experts. In this article, we review and highlight recent studies that use crowdsourcing to advance biomedicine. We classify these studies into two broad categories: (i) mining big data generated from a crowd (e.g. search logs) and (ii) active crowdsourcing via specific technical platforms, e.g. labor markets, wikis, scientific games and community challenges. Through describing each study in detail, we demonstrate the applicability of different methods in a variety of domains in biomedical research, including genomics, biocuration and clinical research. Furthermore, we discuss and highlight the strengths and limitations of different crowdsourcing platforms. Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine.
引用
收藏
页码:23 / 32
页数:10
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