Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy.
Cancer is a disease resulting from genome alterations. Contemporary biotechnologies make genome-scale data from individual patients readily available, and personalized precision medicine based on specific genomic alterations of individual tumors presents promises of more effective therapy. However, there are 3 major gaps hinder the translation from genome data to personalized precision therapy.
JoAnna Hillman, MPH, Doctoral Fellow
Title: Improving Situational Awareness in a High-Burden Labor and Delivery Suite in Malawi
Abstract: This talk will address how advances in Machine Learning, especially used within a “Big Data” paradigm and exploiting new data generation and capture capabilities, are positioned to catalyze radical advances of Medical Science and the transformation of Healthcare.