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机器学习在神经精神疾病诊断及预测中的应用

文宏伟 陆菁菁 何晖光

文宏伟, 陆菁菁, 何晖光. 机器学习在神经精神疾病诊断及预测中的应用[J]. 协和医学杂志, 2018, 9(1): 19-24. doi: 10.3969/j.issn.1674-9081.2018.01.005
引用本文: 文宏伟, 陆菁菁, 何晖光. 机器学习在神经精神疾病诊断及预测中的应用[J]. 协和医学杂志, 2018, 9(1): 19-24. doi: 10.3969/j.issn.1674-9081.2018.01.005
Hong-wei WEN, Jing-jing LU, Hui-guang HE. Application of Machine Learning in Diagnosis and Prediction of Neuropsychiatric Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 19-24. doi: 10.3969/j.issn.1674-9081.2018.01.005
Citation: Hong-wei WEN, Jing-jing LU, Hui-guang HE. Application of Machine Learning in Diagnosis and Prediction of Neuropsychiatric Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 19-24. doi: 10.3969/j.issn.1674-9081.2018.01.005

机器学习在神经精神疾病诊断及预测中的应用

doi: 10.3969/j.issn.1674-9081.2018.01.005
基金项目: 

国家自然科学基金 91520202

国家自然科学基金 81671651

中科院青年创新促进会项目 2012124

中科院仪器项目 YJKYYQ20170050

详细信息
    通讯作者:

    何晖光 电话:010-82544621, E-mail:huiguang.he@ia.ac.cn

  • 中图分类号: R741.049

Application of Machine Learning in Diagnosis and Prediction of Neuropsychiatric Diseases

More Information
  • 摘要: 多模态磁共振神经影像技术的发展为大脑工作原理研究及脑部疾病早期诊断提供了新的手段。当前多数神经精神性疾病的诊断仅依据其临床症状, 缺少客观的神经影像生物学标志物。传统的基于组间比较的单变量分析仅能在组间水平进行推断, 无法提供个体水平的诊断和预测, 临床应用价值非常有限。机器学习技术可在不同水平对神经影像进行计算分析和研究, 发现其中规律从而有效预测和分类未知数据, 找出与脑疾病高度相关的脑区特征, 提供个体水平的诊断并探测脑疾病的病理生理机制。本文对基于机器学习的神经影像分析步骤及机器学习在神经精神疾病智能诊断及预测研究中的应用进行综述, 并对未来研究发展进行展望。
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出版历程
  • 收稿日期:  2017-07-10
  • 刊出日期:  2018-01-30

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