It was my pleasure to be invited to present as part of the Douglas Research Centre’s Cerebral Imaging Centre Lecture Series. Thanks to Jana Totzek for the invitation.

Talk Title

Data-driven Disease Progression Modelling: thinking outside the black box

Abstract

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to ‘black box’ machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification.

This talk is based on my recent Nat Rev Neurosci paper “Data-driven modelling of neurodegenerative disease progression: thinking outside the black box.” URL

PDF slide deck: here

Further Reading:

• Outside the Black Box, Nature Reviews Neuroscience 2024.
• Imaging Plus X, Current Opinion in Neurology 2017.
• Data-Driven Disease Progression Modelling, in the free eBook Machine Learning for Brain Disorders (Ed: O. Colliot) 2023.
• D3PM Jupyter Notebook Tutorials