基于机器学习的预测模型对抑郁症的研究进展

Research Progress on Predictive Models Based on Machine Learning for Depression

随着机器学习方法的兴起,越来越多的研究将预测模型纳入神经领域的研究中,尤其在抑郁症的研究中做了大量工作,但是存在研究结果稳定性的差异。目前通过机器学习的方法实现对抑郁症个体差异的预测以及治疗。本文总结了:1) 预测模型的构建;2) 抑郁症预测的研究现状;3) 预测中存在的问题和当前的总结;4) 对抑郁症的诊断和意义的展望。总体实现了通过预测模型实现对抑郁症的诊断,但是在神经预测的研究的一致性上需要更多的证据或者运用元分析的方法实现。未来结合临床提高抑郁症的诊断和治疗。

With the rise of machine learning methods, more and more studies have incorporated predictive models into the field of neuroscience research, especially in the study of depression, but there is an interpretation of the differences in the stability of research results. The prediction and treat-ment of individual differences in depression are currently achieved through machine learning. This paper summarizes: 1) the construction of predictive models;2) the current research status of depression prediction;3) the problems and current conclusions in the prediction;4) the diagnosis and significance of depression. The overall diagnosis of depression is achieved through predictive models, but more evidence is needed for the consistency of neural prediction studies or by meta-analysis. In the future, study should be combined with clinical to improvement the diagnosis and treatment of depression.