Machine Learning for Medicine: Insights and Interpretability

This lecture by the instructor delves into the application of machine learning in medicine, focusing on the variability between patients and the importance of interpretability in models. The lecture covers the collaboration between machine learning and medicine, the challenges of interdisciplinary work, and the development of machine learning tools for medicine. The instructor discusses the need for augmenting clinicians with machine learning tools, the importance of interpretability in time series forecasting, and the discovery of governing equations in medicine. The lecture also touches on the use of symbolic regression to convert black box models into white box equations, the exploration of ODEs from data, and the potential of neural Laplace models. The instructor emphasizes the significance of understanding human decision-making processes and the quest for transparent equations in medical models.