Dr. Vaughn tries to understand and diagnose aspects of human consciousness using machine learning & neuroimaging (fMRI, DTI, EEG). How does a three-pound cluster of cells in our head generate our experience of the world?
Dr. Vaughn employs these techniques outside of neuroscience. In a collaboration with doctors at the David Geffen School of Medicine, he applied machine learning methods to a database of insurance claims to predict and prevent hospitalizations in patients with inflammatory bowel disease (IBD). The results of this analysis suggested a care pathway that could save Anthem up to 10% on hospitalization expenses related to managing IBD.
Dr. Vaughn’s passion is to make science engaging, accessible and relatable. As a QCBio Collaboratory Fellow, he teaches a seminar on modern statistical methods (permutations, machine learning, bootstrapping) to UCLA faculty, postdoctoral fellows, and students.
Feusner, J. D. et al. Cross-diagnostic Prediction of Dimensional Psychiatric Phenotypes in Anorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data. in Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities 92–99 (Springer International Publishing, 2018).
Eagleman, S. et al. Do complexity measures of frontal EEG distinguish loss of consciousness in geriatric patients under anesthesia? Front. Neurosci. 12, 645 (2018).
McDonald, A. I. et al. Endothelial Regeneration of Large Vessels Is a Biphasic Process Driven by Local Cells with Distinct Proliferative Capacities. Cell Stem Cell 23, 210–225.e6 (2018).
Vaughn, D. A., Savjani, R. R., Cohen, M. S. & Eagleman, D. M. Empathic Neural Responses Predict Group Allegiance. Front. Hum. Neurosci. 12, 302 (2018).
Vaughn, D. A. et al. Using insurance claims to predict and improve hospitalizations and biologics use in members with inflammatory bowel diseases. J. Biomed. Inform. 81, 93–101 (2018).