Disease Progression Modelling

Alzheimer's disease (AD), and dementia in general, is a key challenge for 21st-century healthcare. There are no available treatments that can cure or even halt the progression of AD – all clinical trials into putative treatments have failed to prove a disease-modifying effect. A key reason for these failures is the difficulty in identifying groups of patients at early stages of the disease, where treatments are most likely to be effective. For early identification of subjects at risk of AD, one can use biological markers for estimating a disease signature, which can then be used for predicting the future evolution of these subjects. 

But longitudinal data sets of biomarkers of AD generally lack of a well-defined temporal reference: the onset of the pathology varies across individuals according to genetic, demographic and environmental factors. Therefore, age or visit date information are biased time references for the individual longitudinal measurements. 

Disease progression modelling (DPM) aims to define the AD evolution in a data-driven manner with respect to an absolute time scale associated to the natural history of the pathology, without relying on clinical diagnoses or estimates of time to symptom onset.

If you want to know more, check out our Disease Progression Modelling website, uniting medics with researchers and engineers across the physical and life sciences to tackle some of the biggest challenges of 21st-century medicine by harnessing the power of mathematics, computer science, and data.

Here some software I produced over the years: