基于机器学习的神经精神疾病辅助诊断研究进展

作者：Lei Bingye; Pan Jiayu; Wu Fengchun; Lu Xiaobing; Ning Yuping; Chen Jun; Wu Kai^*

来源：中国医学物理学杂志, 2020, 37(2): 257-262.

DOI：10.3969/j.issn.1005-202X.2020.02.022

• auxiliary diagnosis
• machine learning
• neuroimaging
• neuropsychiatric disease

Neuroimaging techniques are widely used to study the correlations between brain structural/functional abnormalities and neuropsychiatric diseases. Different from traditional statistical analysis methods, machine learning model can realize individualized prediction from neuroimaging data and exploit potential biomarkers. The auxiliary diagnosis of neuropsychiatric diseases includes data preprocessing and machine learning algorithms. Data preprocessing is a kind of artificial feature engineering, providing quantitative features for machine learning algorithms; and machine learning algorithms include feature dimensionality reduction, model training and model evaluation. Robust machine learning algorithms can accomplish accurate predictions for different datasets and provide features that contribute significantly to the prediction as potential biomarkers. Herein the recent advances in auxiliary diagnosis of neuropsychiatric diseases based on machine learning are summarized, including data preprocessing, machine learning algorithms and biomarkers found in previous studies. Finally, the research direction in the future is discussed. ? 2020 Chinese Medical Journals Publishing House Co.Ltd.

• 单位
  广州医科大学; 华南理工大学