Digital Health Innovation and Informatics

EDU 24 - Introduction to Big Data Analytics and Artificial Intelligence

9/18/2019
8:00 AM - 9:00 AM
Location: Room W183

Session Type: Education
1.00 AMA PRA Category 1 Credits™
1.00 CAMPEP Credits
1.00 MDCB Credits

Currently the applications of big data (BD) and artificial intelligence (AI) in the radiation oncology community have been dramatically increasing, as manifested by a huge surge of journal articles and conference sessions on the two subjects. The trend is expected to continue into a foreseeable future. Yet, there exists a knowledge gap in our field where the basics of big data analytics and artificial intelligence are not clearly understood, which may hinder their future applications in our clinics. In this session, we aim to fulfill this knowledge gap by answering the following questions: (1) How do we deal with noisy and missing data? (2) How do modern machine learning (ML) algorithms compare with simple models in the past like logistic regression model? (3) Why do we choose one algorithm over the other? (4) How do we evaluate the performance of a specific ML algorithm? (5) What do the ROC, AUC, sensitivity, specificity, PPV, NPV and accuracy mean? (6) How robust are our ML algorithms? (7) Are there any examples showing the applications of AI in radiation oncology? At the end of this session, we hope to see a clearer understanding of the basic concepts in big data analytics and artificial intelligence by the attendees and more widespread applications of BD and AI in the field for more personalized radiotherapy and cancer management.

Learning Objectives:

Presentations:

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Jun Deng, PhD

Yale University

Disclosure:
Employment
Yale University: Professor: Employee

Compensation
NIH: Research Grants

Biography:
Dr. Jun Deng is a Professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. Dr. Deng obtained his Ph.D. from University of Virginia in 1998 and finished his postdoctoral fellowship at Department of Radiation Oncology of Stanford University in 2001. Dr. Deng has been serving on the Editorial Board of numerous peer-reviewed journals, on the study section of NIH, DOD, ASTRO and RSNA since 2005, and as scientific reviewer for European Science Foundation, UK Cancer Research and Dutch Cancer Society since 2015. Dr. Deng has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. At Yale, Dr. Deng’s research has been focused on big data, machine learning, artificial intelligence, and medical imaging for early cancer prediction, detection and prevention.

Presentation(s):

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