Hosted by The Alan Turing Institute @Bristol

Abstract: Early prediction of brain health (e.g. neurodegenerative) disorders is key for clinical management and patient outcomes. Predicting whether individuals with mild cognitive impairment or people without symptoms will decline or remain stable is impeded by patient heterogeneity due to comorbidities, lifestyle and disease severity. Despite the importance of early diagnosis of dementia for prognosis and personalised interventions, we still lack robust tools for predicting individual progression.

We propose a novel clinical AI predictive prognostic modelling approach that mines multimodal data to derive an individualised prognostic marker of cognitive decline at early stages of dementia or before symptoms occur. We validate our approach against routinely collected real-world patient data from memory clinics over time, showing that our clinical AI marker is more sensitive than the standard of care (cognitive tests, MRI scans). Our clinical AI approach has strong potential to facilitate effective patient stratification into clinical pathways and trials, reducing patient misdiagnosis with important implications for clinical translation and drug discovery.

Bio: Zoe’s research aims to develop predictive models of neurodegenerative disease and mental health with translational impact in early diagnosis and personalised interventions. Zoe received her PhD from Rutgers University and was a postdoctoral fellow at MIT and Harvard.

She was a Senior Research Scientist at the Max Planck Institute for Biological Cybernetics and then a Chair in Brain Imaging at the University of Birmingham, before moving to the University of Cambridge in 2013. She is a Royal Society Industry Fellow, Cambridge University Lead at the Alan Turing Institute and Co-director of Cambridge’s Centre for Data Driven Discovery.’