Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.

ABSTRACT:

Background

Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.

Methods

We searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023. QUADAS-2 was applied to assess the quality of the literature included.

SUBMITTER: Pan Y 

PROVIDER: S-EPMC11810903 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature