Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)
This is an incremental response aimed at clarifying methodological issues in psychiatric subtyping research.
The authors address criticisms of their prior work on using machine learning to identify neurobiological subtypes of major psychiatric disorders, focusing on misconceptions about generalizability, statistical significance, and overfitting, without presenting new results or numbers.
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.