论文标题
对：主要精神疾病中亚型基于深度学习的鉴定的意义和稳定的反应。分子精神病学（2022）
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)
论文作者
论文摘要
最近，Winter and Hahn [1]评论了我们关于使用机器学习的神经生物学特征鉴定主要精神病障碍（MPD）亚型的工作[2]。他们质疑我们方法的普遍性以及结果的统计意义，稳定性和过度拟合，并提出了疾病亚型的管道。我们感谢他们对我们的工作的认真考虑，但是，我们需要指出他们对基本机器学习概念的误解，并描述涉及的一些关键问题。
Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features using machine learning [2]. They questioned the generalizability of our methods and the statistical significance, stability, and overfitting of the results, and proposed a pipeline for disease subtyping. We appreciate their earnest consideration of our work, however, we need to point out their misconceptions of basic machine-learning concepts and delineate some key issues involved.
