通过排名和大学习进行医学社交媒体分析:基于图像的疾病预测研究

Wei Huang, Peng Zhang, Minmin Shen
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引用次数: 1

摘要

医学社交媒体分析如今变得越来越流行,因为它在各种医疗保健应用中都很有效。在本研究中,通过医学社交媒体分析技术来研究和实现基本疾病预测任务。其中,动脉自旋标记(ASL)是一种新兴的功能磁共振成像方式,可以提供基于图像的信息,并提出了新的排序和学习技术来完成痴呆症的疾病预测任务。为了证明该方法的优越性,进行了综合统计实验,并与几种传统方法进行了比较。这项研究报告了令人鼓舞的结果。
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Medical social media analytics via ranking and big learning: An image-based disease prediction study
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.
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