CIS Master's Thesis Defense by Jacob Matos
Advisor: Dr. (Julia) Hua Fang
Committee members: Dr. Yuchou Chang and Dr. Adnan El-Nasan
Title: Generating Biomarker-Based Digital Twins Through Character Creation and Twin Health Prediction
Abstract:
Over the past few decades, digital twins have grown in popularity as a tool in simulation and data visualization. Digital twins have shown themselves to be a powerful tool in the manufacturing and construction industry and have more recently gained intrigue in the healthcare industry, especially in collaboration with artificial intelligence. With the introduction of digital twins into healthcare being so fresh, there are a lot of use cases for digital twins that have yet to be implemented in any way. Digital twins allow for interactive, detailed, and easy to understand visualizations of a patient’s well-being in the form of a virtual 3D model of a patient or a related body part to help bridge the gap between professionals to patient understanding. This thesis discusses the current state of digital twins in healthcare data visualization, focuses on the development of an algorithm to convert longitudinal random control test (RCT) data from the National Institutes of Health's (NIH) dataset into 3D digital twins, and expands upon the uses of this algorithm by applying it to predicted versions of participants derived from the NIH data using linear regression and random forest regression. This algorithm serves as a basis for the automated development of digital twins using any
dataset that contains biomarker data on its participants.
All CIS students are encouraged to attend, and all interested parties are invited.
For further information, please contact Dr. Hua Fang.
Zoom
Julia Fang
X8457
hfang2@umassd.edu
https://umassd.zoom.us/j/95683754446?pwd=WW1paGw5Q29mdXBMb0E3N3dkUTZ2Zz09