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

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

文宏伟^{1, 2, 3},
陆菁菁⁴,
何晖光^{1, 2, 5, ,}

1.
中国科学院自动化研究所类脑智能研究中心
2.
中国科学院大学, 北京 100049
3.
西南大学心理学部, 重庆 400715
4.
中国医学科学院北京协和医学院北京协和医院放射科, 北京 100730
5.
中国科学院脑科学与智能技术卓越创新中心, 北京 100190

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
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- 文章访问数: 342
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- PDF下载量: 809
- 被引次数: 0
出版历程
- 收稿日期: 2017-07-10
- 刊出日期: 2018-01-30

Application of Machine Learning in Diagnosis and Prediction of Neuropsychiatric Diseases

Hong-wei WEN^{1, 2, 3},
Jing-jing LU⁴,
Hui-guang HE^{1, 2, 5
, ,}

1.
Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Department of Psychology, Southwest University, Chongqing 400715, China
4.
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
5.
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China

More Information

Corresponding author: HE Hui-guang Tel:010-82544621, E-mail:huiguang.he@ia.ac.cn

摘要

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。
- 机器学习 /
- 磁共振成像 /
- 神经精神疾病 /
- 分类预测 /
- 个体诊断
Abstract: The development of multi-modal magnetic resonance imaging (MRI) provides a new method for early diagnosis of brain diseases. Currently, diagnosis of most neuropsychiatric diseases is only based on clinical symptoms, which lacks objective neuroimaging biomarkers. As univariate analysis that is used in traditional research can only reveal disease-related structural and functional alterations at group level, which limits the clinical application. Recent attention has turned toward integrating multi-modal neuroimaging and machine learning to assist clinical disease diagnosis. Machine learning can obtain rules via automatically analyzing neuro-imaging data, and apply these rules to predict unknown data, to identify the brain regions highly correlated to the brain disease, to provide individual levels of diagnosis, and to detect pathophysiological mechanisms. This paper reviews the concrete steps of neuroimaging analysis based on machine learning and the application of machine learning in intelligent diagnosis and prediction of neuropsychiatric diseases. Finally, some new directions for future research are forecasted.
- machine learning /
- magnetic resonance imaging /
- neuropsychiatric disease /
- classification & prediction /
- individual diagnosis

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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

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

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Application of Machine Learning in Diagnosis and Prediction of Neuropsychiatric Diseases

Corresponding author: HE Hui-guang Tel:010-82544621, E-mail:huiguang.he@ia.ac.cn

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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

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

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出版历程

Application of Machine Learning in Diagnosis and Prediction of Neuropsychiatric Diseases

Corresponding author: HE Hui-guang Tel:010-82544621, E-mail:huiguang.he@ia.ac.cn

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出版历程

目录

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