About this Research Topic

Manuscript Submission Deadline 26 September 2022
Manuscript Extension Submission Deadline 24 October 2022

Hardware and software supporting user-driven self-tracking of behavioral and health data metrics are rapidly scaling in availability, impact and integration with other technologies. With passive data collection tools for monitoring physical activity, sleep, and biodata (e.g. pulse, heart rate variability) ...

Hardware and software supporting user-driven self-tracking of behavioral and health data metrics are rapidly scaling in availability, impact and integration with other technologies. With passive data collection tools for monitoring physical activity, sleep, and biodata (e.g. pulse, heart rate variability) becoming a common component of daily commercial technology, it is a thrilling era for real-time and continuous monitoring of biopsychosocial determinants of current and future health. The application of Artificial intelligence (AI) based computational models, including Deep learning, recurrent neural networks, and agent based models, have been applied to cognitive and neuroscience data for decoding, generative modelling, predicting group membership and neurocognitive health outcomes. As such, the intersection of these advanced AI analytic approaches to quantified self data affords numerous exciting avenues for investigation.

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.

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