Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for stratified interventions to the development of personalised interactions supporting self-care between clinic visits. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way. Within the healthcare domain, machine learning for mental healthcare is an under-investigated area and yet a potentially highly impactful area of research. In this talk, I will present recent work on probabilistic graphical modelling to enable a more personalised approach to mental healthcare, whereby information can be aggregated from multiple sources within a unified modelling framework. We present a human-centred approach to mental healthcare which is aimed at increasing the effectiveness of psychological wellbeing practitioners.
Dr. Danielle Belgrave is a Principal Researcher Manager at Microsoft Research, in Cambridge (UK) in the Health Intelligence group where she leads Project Talia. She is particularly interested in integrating medical domain knowledge to develop probabilistic graphical models to develop personalized treatment strategies in health. Originally from Trinidad and Tobago, she received her BSc in Business Mathematics and Statistics from London School of Economics, an MSc in Statistics from University College London and her PhD in Machine Learning and Statistics for Healthcare from The University of Manchester where she was a Microsoft Research PhD scholar. Prior to joining Microsoft Research, she had a tenured faculty position at Imperial College London.
See full report on Seminar 9