An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health - Boulos, Laura Joy; Mendes, Alexandre; Delmas, Alexandra; Chraibi Kaadoud, Ikram (Frontiers in Psychiatry, 2021)
Artificial Intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of neurologic and psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector, and to centralize data into federalizing platforms. Here we propose an end-to-end methodology that optimizes and homogenizes research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides a guideline for upcoming digital mental health studies, with the accompany the translation of fundamental mental health research to digital technologies.