Mathematical and Computational Frameworks for Adaptively Benchmarking Patients in States of Health, Disease, and Recovery
Jump to:
Bio
“Mathematical and Computational Frameworks for Adaptively Benchmarking Patients in States of Health, Disease, and Recovery”
Dr. Brody Foy is a junior faculty member at the University of Washington, Department of Laboratory Medicine & Pathology. Brody completed his DPhil in Computer Science at the University of Oxford, as a Rhodes Scholar, and undertook postdoctoral training at Harvard Medical School & Massachusetts General Hospital. A mathematician by training, Brody’s research is focused on using math modelling and machine learning to improve utilization of clinical laboratory data, and increase the quality of information extraction from blood testing. His lab uses computational tools to learn about human physiology, improve clinical workflows, and develop novel tools for patient care.
Abstract
“Mathematical and Computational Frameworks for Adaptively Benchmarking Patients in States of Health, Disease, and Recovery”
Laboratory testing is a cornerstone of modern medicine. While cutting-edge assays are constantly in development, the bulk of worldwide clinical testing is dominated by only a handful of markers. These ‘boring’ markers are regularly used in patient evaluation – but the physiologic insights they can provide are often overlooked. In this talk I will explore how mathematical and statistical methods can be used to generate deep clinical and physiologic insights from routine clinical laboratory tests such as the complete blood count. From my own research I will show how careful analysis and modelling of biomarker dynamics can provide exciting and novel insights into homeostatic recovery and regulation, chronic illness, and physiologic shifts such as pregnancy and menopause.
