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Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for patient stratification to the development of personalised interactions and interventions. As medicine pivots from treating diagnoses to treating mechanisms, there is an increasing need for personalised health through more intelligent feature extraction and phenotyping. Health presents some of the most challenging and under-investigated domains of machine learning research. In this talk, I will present a flexible framework for discovery of subgroups of disease through the application of probabilistic modelling to disambiguate diseases where there are heterogeneous phenomena. This strategy enables us to develop a more personalised approach to healthcare whereby information can be aggregated from multiple sources within a unified modelling framework. The work presented will focus on disaggregating complex evolving disease endotypes which will enable the discovery of clinically meaningful subgroups of asthma phenotypes.
Danielle Belgrave is a machine learning researcher in the Healthcare AI Research Group at Microsoft Research Cambridge. Her research focuses on integrating medical domain knowledge to develop statistical machine learning models to understand disease progression and heterogeneity. 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. She is also a Medical Research Council Fellow at Imperial College London