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arXiv 1704.05960cs.LGstat.ML
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SAFS:一种用于精准医学的深度特征选择方法

SAFS: A Deep Feature Selection Approach for Precision Medicine

Wayne State University(韦恩州立大学)

Milad Zafar Nezhad, Dongxiao Zhu, Xiangrui Li, Kai Yang, Phillip Levy

摘要:

本文提出了一种新的基于深度体系结构的深度特征选择方法。我们的方法使用堆叠的自动编码器在更高级别的抽象中进行特征表示。我们开发并应用了一种新的特征学习方法来解决特定的精准医学问题,该方法侧重于评估和优先考虑弱势人口亚组(非洲裔美国人)中高血压(HTN)的风险因素。我们的方法是使用深度学习来识别影响以体表面积(LVMI)为指标的左心室质量的重要风险因素,作为心脏损伤风险的指标。结果表明,与其他方法相比,我们的特征学习和表示方法导致更好的结果。

英文摘要:

In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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