Machine Learning for Personalised Healthcare: Opportunities, Challenges and Insights

Seminar / Lecture
Open to the Public

Geometric abstract image of brain

Part of the Special ECE Seminar Series 

Modern Artificial Intelligence


Machine Learning for Personalised Healthcare: Opportunities, Challenges and Insights


Danielle Belgrave, machine learning researcher in the Healthcare Intelligence group at Microsoft Research Cambridge

Niranjani Prasad, senior researcher in the Healthcare Intelligence group at Microsoft Research Cambridge




Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for stratified interventions to the development of tailored sequences of interaction. These methods offer an exciting opportunity to have a meaningful impact on the delivery of healthcare. In this talk, we will present some of the inroads of machine learning for understanding and learning personalised interventions. Taking examples from mental health, respiratory disease and critical care settings, we present some of the opportunities and inherent challenges to leveraging machine learning in healthcare towards actionable insights.


Danielle BelgraveDr. Danielle Belgrave is a machine learning researcher in the Healthcare Intelligence group at Microsoft Research, in Cambridge (UK) where she works on Project Talia.  Her research focuses on integrating medical domain knowledge, probabilistic graphical modelling and causal modelling frameworks to help develop personalized treatment and intervention strategies for mental health. Mental health presents one of the most challenging and under-investigated domains of machine learning research. In Project Talia, they explore how a human-centric approach to machine learning can meaningfully assist in the detection, diagnosis, monitoring, and treatment of mental health problems. She obtained a BSc in Mathematics and Statistics from London School of Economics, an MSc in Statistics from University College London and a Ph.D. in the area of machine learning in health applications from the University of Manchester. Prior to joining Microsoft, she was a tenured Research Fellow at Imperial College London.

Niranjani PrasadDr. Niranjani Prasad is a senior researcher in the Health Intelligence team at Microsoft Research Cambridge, developing methods to guide personalized interventions in online mental health services. Her research draws on machine learning frameworks for automated decision support such as reinforcement learning and causal inference. She obtained her undergraduate degree (BA,MEng) in Information and Computer Engineering from the University of Cambridge. Prior to joining Microsoft, she completed her Ph.D. in Computer Science at Princeton University, advised by Professor Barbara Engelhardt, where her work centred on clinician-in-loop sequential decision-making in the critical care setting.