Challenges for Delivering Machine Learning in Health

[edit]

at Deep Learning in Healthcare Summit, London on Feb 28, 2017 [reveal]
Neil D. Lawrence, University of Sheffield

Abstract

The wealth of data availability presents new opportunities in health but also challenges. In this talk we will focus on challenges for machine learning in health: 1. Paradoxes of the Data Society, 2. Quantifying the Value of Data, 3. Privacy, loss of control, marginalization. Each of these challenges has particular implications for machine learning. The paradoxes relate to our evolving relationship with data and our changing expectations. Quantifying value is vital for accounting for the influence of data in our new digital economies and issues of privacy and loss of control are fundamental to how our pre-existing rights evolve as the digital world encroaches more closely on the physical. One of the goals of research community should be to provide the technological tooling to address these challenges ensure that we are empowered to avoid the pitfalls of the data driven society, allowing us to reap the benefits of machine learning in applications from personalized health to health in the developing world.

Links