About this Research Topic
The goal of this research topic is to present forefront advances in the application of machine learning and big data analytics in the field of psychiatric disease, especially mood disorders. To target clinical problems with the best services and care, applying techniques of machine learning and big data analysis based on high-quality data can understand the patient’s status and further support clinical decision-making. The results of the current topic can benefit the early identification, phenotype, treatment, prognosis, and prevention of mental health disorders. This topic will highlight the pronounced advantages of machine learning and big data analytics in the research of mood disorders to improve the diagnosis and management of patients. This collection of articles will also fulfill the insights of the experts and data researchers to interpret and transform rich psychiatric datasets into clinical health practice and public health systems.
We welcome submissions including but not limited to the following topics:
• Development and application of machine learning and big data analytical methods in research on mood disorders.
• Descriptive observations of machine learning and big data analytics in mood disorders, including the genetic or biomarker characteristics, characteristics or trends of treatment patterns, the progress of the disease, etc.
• Redefining patient classes or differentiating patient subgroups based on machine learning and big data analytics.
• Development and validation of prediction model based on machine learning and big data analytics to inform clinical decision-making for mood disorders.
• Implementations of real-time alerts or decision-making systems for mood disorders based on machine learning and big data analytics.
Keywords: Big data analysis, machine learning, precision psychiatry, mood disorders, depressive disorders, bipolar disorder
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.