This post refers to a newly published paper involving members of the van der Schaar Lab inside CCAIM. Find out more here.
In their paper, Changhee Lee, Alexander Light, Evgeny Saveliev, Mihaela van der Schaar, and Vincent Gnanapragasam present the first machine learning application for a dynamic risk prediction and temporal clustering of continuous data. This offers the opportunity to inform personalised follow-ups in prostate cancer active surveillance patients. Their algorithm, surpassing the current standards significantly, has the potential to be developed into a useable clinical tool.
To learn more about the model, using cutting-edge machine learning (deep learning) to cut out assumptions and constraints of traditional statistical methods, you can discover an extensive explanative post on the van der Schaar Lab website here:
To illustrate the potential utility of their work, the authors provide an interactive web app, demonstrating a possible future clinical platform, which we encourage the reader to try out. More information about this can be found following the link above.
The paper is accessible here: