Application of machine learning in bipolar disorder

Purpose of review: Machine learning, a hot area of research today, has been attempted to be applied in various fields of clinical medicine. This paper reviews the current status of research on machine learning in the filed of bipolar disorder, including applications in the diagnosis, identification and the accurate treatment of bipolar disorder, and the prediction of suicidal behaviors in bipolar disorder. It also discusses the advantages and shortcomings and prospects of the applications of machine learning in bipolar disorder, in order to provide references for related researches and clinical treatment. Research methods: The literature on the application of machine learning in the field of bipolar disorder in recent years was searched through literature navigation. The aspects of prevention, diagnosis and treatment interventions in bipolar disorder were analyzed. Research conclusion: In recent years, researches on the clinical diagnosis and treatment of bipolar disorder by machine learning have been increasing. However, there are still some limitations and it still needs to be cautious in clinical application. As mental health practitioners, we should actively adapt to and promote the further development of machine learning in the field of bipolar disorder.