AI for precision mental health

Predicting mental health early and precisely has major implications for clinical management and practice, and ultimately life expectancy. 

This project will use machine learning techniques to produce robust modelling tools that aim to improve the precision of clinical practice in mental health. Machine learning will be used to predict and classify disease risk at an individual level (for dementia, anxiety, depression, and others) and to determine the interactive factors that influence mental health across people’s lifespans (e.g. genetics, cognition,demographics). 

Alan Turing Institute Fellowship to Zoe Kourtzi: TU/B/000095