Sep 9–12, 2019
Dexter Hadley

Dexter Hadley
Assistant Professor of Pedatrics, Pathology and Laboratory Medicine, University of California, San Francisco

Website

Dexter Hadley as an assistant professor of pedatrics, pathology, and laboratory medicine at the University of California, San Francisco (UCSF). His expertise is in translating big data into precision medicine and digital health. His background is in genomics and computational biology, and he has training in clinical pathology. His research generates, annotates, and ultimately reasons over large multimodal data stores to identify novel biomarkers and potential therapeutics for disease. His early work resulted in a successful precision medicine clinical trial for ADHD (Clinicaltrials.gov identifier NCT02286817) for a first-in-class, nonstimulant neuromodulator to be targeted across the neuropsychiatric disease spectrum. More recently, his laboratory was funded by the NIH Big Data to Knowledge Initiative to develop the Stargeo.org online portal to crowdsource annotations of open genomics big data that allows users to discover the functional genes and biological pathways that are defective in disease. He also develops state-of-the-art data-driven models of clinical intelligence that drive clinical applications to more precisely screen, diagnose, and manage disease. Toward this end, he has been recognized by UCSF with various awards including the inaugural UCSF Marcus Award for Precision Medicine to develop a digital learning health system to use smartphones to screen for skin cancer as well as a pilot award in precision imaging to better screen mammograms for invasive breast cancer. In general, the end point of his work is rapid proofs of concept clinical trials in humans that translate into better patient outcomes and reduced morbidity and mortality across the spectrum of disease.

Sessions

4:00pm4:40pm Wednesday, September 11, 2019
Location: LL21 E/F
Dexter Hadley (University of California, San Francisco)
Average rating: *****
(5.00, 1 rating)
Typically, large healthcare institutions have large-scale quantities of clinical data to facilitate precision medicine through an AI paradigm. However, this hardly translates into improved care. Dexter Hadley details how UCSF uses NLP to curate clinical data for over 1M mammograms and how deep learning, blockchain, and other approaches translate this into precision oncology. Read more.

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