Medical social media analytics via ranking and big learning: An image-based disease prediction study

Wei Huang; Peng Zhang; Minmin Shen

doi:10.1109/SPAC.2014.6982722

Medical social media analytics via ranking and big learning: An image-based disease prediction study

Wei Huang, Peng Zhang, Minmin Shen

计算机学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.

源语言	英语
主期刊名	Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014
出版商	Institute of Electrical and Electronics Engineers Inc.
页	389-394
页数	6
ISBN（电子版）	9781479953530
DOI	https://doi.org/10.1109/SPAC.2014.6982722
出版状态	已出版 - 11 12月 2014
活动	2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014 - Wuhan, Hubei, 中国期限: 18 10月 2014 → 19 10月 2014

出版系列

姓名	Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014

会议

会议	2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014
国家/地区	中国
市	Wuhan, Hubei
时期	18/10/14 → 19/10/14

访问文件

10.1109/SPAC.2014.6982722

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链接到 Scopus 的出版物

引用此

Huang, W., Zhang, P., & Shen, M. (2014). Medical social media analytics via ranking and big learning: An image-based disease prediction study. 在 Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014 (页码 389-394). 文章 6982722 (Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPAC.2014.6982722

Huang, Wei ; Zhang, Peng ; Shen, Minmin. / Medical social media analytics via ranking and big learning : An image-based disease prediction study. Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 页码 389-394 (Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014).

@inproceedings{091409c7c565447789b41f84d10310b6,

title = "Medical social media analytics via ranking and big learning: An image-based disease prediction study",

abstract = "Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.",

author = "Wei Huang and Peng Zhang and Minmin Shen",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014 ; Conference date: 18-10-2014 Through 19-10-2014",

year = "2014",

month = dec,

day = "11",

doi = "10.1109/SPAC.2014.6982722",

language = "英语",

series = "Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "389--394",

booktitle = "Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014",

}

Huang, W, Zhang, P & Shen, M 2014, Medical social media analytics via ranking and big learning: An image-based disease prediction study. 在 Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014., 6982722, Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014, Institute of Electrical and Electronics Engineers Inc., 页码 389-394, 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014, Wuhan, Hubei, 中国, 18/10/14. https://doi.org/10.1109/SPAC.2014.6982722

Medical social media analytics via ranking and big learning: An image-based disease prediction study. / Huang, Wei; Zhang, Peng; Shen, Minmin.
Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 页码 389-394 6982722 (Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Medical social media analytics via ranking and big learning

T2 - 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014

AU - Huang, Wei

AU - Zhang, Peng

AU - Shen, Minmin

PY - 2014/12/11

Y1 - 2014/12/11

N2 - Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.

AB - Medical social media analytics becomes more and more popular nowadays because of its effectiveness in benefiting diverse health-care applications. In this study, the essential disease prediction task is investigated and realized via medical social media analytics techniques. To be specific, arterial spin labeling (ASL), an emerging functional magnetic resonance imaging modality, is utilized to provide image-based information and novel ranking as well as learning techniques are proposed and incorporated to fulfill the disease prediction task in dementia. To demonstrate its superiority, comprehensive statistical experiments are conducted with comparison to several conventional methods. Promising results are reported from this study.

UR - http://www.scopus.com/inward/record.url?scp=84920735598&partnerID=8YFLogxK

U2 - 10.1109/SPAC.2014.6982722

DO - 10.1109/SPAC.2014.6982722

M3 - 会议稿件

AN - SCOPUS:84920735598

T3 - Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014

SP - 389

EP - 394

BT - Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 October 2014 through 19 October 2014

ER -

Huang W, Zhang P, Shen M. Medical social media analytics via ranking and big learning: An image-based disease prediction study. 在 Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 页码 389-394. 6982722. (Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014). doi: 10.1109/SPAC.2014.6982722

Medical social media analytics via ranking and big learning: An image-based disease prediction study

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