About this Research Topic
This Research Topic is mainly focused on the application of artificial intelligence approaches to deconstruct, model and predict current and future cognitive, emotional and behavioral health outcomes using quantified self data.
We particularly welcome Original Research and Review studies, however all article types will be considered. The most interesting areas of research pertinent to this research topic collection include:
● Passive data collection of metrics related to neurocognitive and psychiatric outcomes.
● Application of artificial intelligence models to quantified-self data for decomposition and prediction of future health status.
● Investigation of the factors influencing the performance of deep learning models for predicting neuropsychiatric outcomes.
● Comparison and adjudication amongst different predictive models for biopsychometric data analysis and prediction.
This Research Topic will bring together novel developments in the hardware and software supporting quantified self health monitoring and machine learning in service of predicting brain-based health outcomes. High quality papers from relevant fields are also welcome.
Keywords: machine learning, brain-based health, prediction
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.