The rapid generation and restoration of digital data in mental health have introduced unprecedented renovations in psychiatric research. Applying machine learning and big data analytics can leverage high-quality data to understand and transform the massive amount of information. With the capabilities of yielding better medical profiles and better risk predictions, machine learning and big data analytics are pronounced in supporting the decision-making system for psychiatric patients. The field of machine learning and big data analytics provides opportunities to switch from evidence-based group-level interventions to personalized and tailored care. Mood disorders are the leading cause of disabilities and are the most common psychiatric disorders. Machine learning and big data analytics are increasingly recognized as promising approaches in the research on mood disorders. Despite the advances in the development and applications of innovative techniques and facilities of digital data, there remain challenges in this area such as the control of the data quality, integration of data sources with heterogeneity, analyses of big data with high dimensionality, and the transformation of statistical knowledge.
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.
The rapid generation and restoration of digital data in mental health have introduced unprecedented renovations in psychiatric research. Applying machine learning and big data analytics can leverage high-quality data to understand and transform the massive amount of information. With the capabilities of yielding better medical profiles and better risk predictions, machine learning and big data analytics are pronounced in supporting the decision-making system for psychiatric patients. The field of machine learning and big data analytics provides opportunities to switch from evidence-based group-level interventions to personalized and tailored care. Mood disorders are the leading cause of disabilities and are the most common psychiatric disorders. Machine learning and big data analytics are increasingly recognized as promising approaches in the research on mood disorders. Despite the advances in the development and applications of innovative techniques and facilities of digital data, there remain challenges in this area such as the control of the data quality, integration of data sources with heterogeneity, analyses of big data with high dimensionality, and the transformation of statistical knowledge.
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.
