Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

ABSTRACT:

Background

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

Methods

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psych

SUBMITTER: Chen Z 

PROVIDER: S-EPMC10318841 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature