Balance, bias and size: unlocking the potential of retrospective clinical data for healthcare applications
Since the widespread adoption of IT systems such as PACS (Picture Archival and Communication System) and EMR (Electronic Medical Record), hospitals have been collecting digital patient data as part of clinical practice. Such retrospective archives represent a treasure trove for deep learning applications in healthcare. However, in practice such data is rarely in a form that can be readily utilized for learning due to missing labels, class imbalance and bias. In this talk, I will discuss techniques to address such challenges including self-supervision, multi-task learning and transfer learning and will illustrate using applications in lung cancer and chronic backpain.
Timor Kadir graduated with an MEng in Electrical and Electronic Engineering from Surrey University in 1996 and studied for a DPhil at University of Oxford under Sir Michael Brady. He joined CTI/Siemens as a Research Scientist working on computer vision software and in 2009, during a management buy-out, Timor became CTO for Mirada Medical. Most recently, he founded Optellum, a company delivering machine learning based clinical risk stratification. He is currently the CTO of Optellum, Mirada and visiting fellow at Oxford. He’s published a reasonable number of papers, filed a bunch of patents and his h-index isn’t bad either.