Understanding Heterogeneity as a Route to Understanding Health

Danielle Belgrave / DeepMind

Abstract: Machine learning presents an opportunity to understand the patient journey over high dimensional data in the clinical context. This is aligned to one of the foundational issues of machine learning for healthcare: how do you represent a patient state. Improving state representations allows us to (i) visualise/cluster deteriorating patients, (ii) understand the patient journey and thus heterogeneous pathways to improvement or clinical deterioration which encompasses different data modalities; and thus (iii) more quickly identify situations for intervention. In this talk, I present motivating examples of understanding heterogeneity as a route towards understanding health and personalising healthcare interventions.

Bio: Danielle Belgrave is a Senior Staff Research Scientist at DeepMind. Prior to joining DeepMind she worked in the Healthcare Intelligence group at Microsoft Research and was a tenured research fellow at Imperial College London. Her research focuses on integrating medical domain knowledge, machine learning and causal modelling frameworks to understand health. She obtained a BSc in Mathematics and Statistics from London School of Economics, an MSc in Statistics from University College London and a PhD in the area of machine learning in health applications from the University of Manchester.