Why medicine is creating exciting new frontiers for machine learning
Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19!
In this talk I will show how machine learning is transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in automated machine learning, interpretable and explainable machine learning, dynamic forecasting, and causal inference.
This event will take place online on May 5 at 11:35 BST.
This will be the first external event of the CMIH Hub and aims to bring together those working in mathematical healthcare data analytics across the UK, including academic, clinical, and industrial users with mathematicians working in similar areas.
The event will include talks that highlight open challenges and successes from CMIH Hub researchers and will present other potential collaborative opportunities, as well as projects being developed elsewhere related to healthcare data analytics.
Talks will focus on the key theme of the CMIH Hub, which is the development of robust and clinical applicable algorithms for analysing healthcare data in an integrated fashion. In addition, there will be introductory talks from partner EPSRC Hubs for Mathematical Sciences in Healthcare.
This event will bring together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholders.