Title: Integrating Multiscale Modeling and Machine Learning in Computational Medicine
Affilication: University of Delaware, Computer and Information Sciences Department
Abstract: Computational models have revolutionized our understanding of complex biological systems. These models typically fall into two categories: multiscale models and machine learning models. Multiscale models, grounded in the fundamental principles of physics and chemistry, can dissect the intricate causalities in disease progression. In parallel, machine learning models, based on rich datasets, have yielded unique insights into complex pattern recognition within these systems.
In this talk, I will demonstrate how multiscale models are developed to probe disease-mediated changes in blood dynamics. I will also show how various machine learning models, with a focus on deep learning, are designed to enhance accurate prediction, optimize treatment plans, and distill extensive knowledge of different diseases.
To conclude, I will discuss the pivotal factors—such as age and sex—that are critical to customizing treatments in the realm of precision medicine. I will share how to synergistically integrate multiscale modeling with machine learning to enable the design of more comprehensive personalized medicine.