Ellen Kuhl's KEYNOTE seminar "Opportunities for Machine Learning in Human Health"
Time: Thu 2022-10-13 16.15
Participating: Professor Ellen Kuhl, Stanford University, US
Ellen_Kuhl_ Oct_13_2022.pdf (pdf 163 kB)
Abstract. Understanding real-world dynamical phenomena remains a challenging task. In human health, as much as in many other scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decisions around them. This seminar systematically compares two families of machine learning tools and illustrates their applications in human health: neural networks and Bayesian inference. Neural networks minimize a loss function to optimize the network parameters, without any prior knowledge of the underlying physics. Physics informed neural networks expand the loss function by an additional physics term and not only interpolate the training data, but also extrapolate and predict future behavior. Bayesian inference maximizes a prior-weighted likelihood function to estimate posterior distributions of model parameters. It not only infers model parameters to fit the training data, but also provides credible intervals to quantify the quality of the model. In this talk, we illustrate the potential of neural networks, Bayesian inference, and a combination of both for dynamical systems in human health. We discuss applications to the COVID-19 pandemic, the human heart, and the aging brain with the goal to generalize physics-informed machine learning to a wide variety of nonlinear dynamical systems and open new opportunities for machine learning in the benefit of human health.