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

CIC Imaging Lecture Series lecture by Dr. Neil Oxtoby, Principal Research Fellow, UCL Centre for Medical Image Computing, Department of Computer Science, University College London

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 his recent Nat Rev Neurosci paper “Data-driven modelling of neurodegenerative disease progression: thinking outside the black box.”