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Medicine: Mind the Gap

Automating Machine Learning for Prevention Research

Jason Moore, Ph.D.

Tuesday, August 29, 2017
This Medicine: Mind the Gap lecture was presented as a webinar.

Jason Moore, Ph.D. External Website Policy
Director, Institute for Biomedical Informatics
Perelman School of Medicine
University of Pennsylvania

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About the Webinar

Successful disease prevention will depend on modeling human health as a complex system that is dynamic in time and space and driven by biomolecular and physiologic interactions. Machine learning holds promise for embracing this complexity in Big Data. The intent is to provide a complement to traditional statistical methods that ignore much of this complexity in favor of simpler mathematical models. Unfortunately, the barrier to machine learning is steep, requiring knowledge about many different types of algorithms and methods that need to be combined in an analytical pipeline. The webinar reviewed here the new discipline of automated machine learning (AutoML), which has the goal of simplifying this process and making machine learning more accessible. An example from human genetics was presented.

About Jason Moore, Ph.D.

Jason Moore is the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also serves as Senior Associate Dean for Informatics and Chief of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics. He came to Penn in 2015 from Dartmouth College, where he was Director of the Institute for Quantitative Biomedical Sciences. Prior to Dartmouth, he served as Director of the Advanced Computing Center for Research and Education at Vanderbilt University, where he launched their first high-performance computer. He has a Ph.D. in Human Genetics and an M.S. in Applied Statistics from the University of Michigan. He leads an active NIH-funded research program focused on the development of artificial intelligence and machine learning algorithms for the analysis of complex biomedical data. He is an elected fellow of the American Association for the Advancement of Science (AAAS), an elected fellow of the American College of Medical Informatics (ACMI), an elected fellow of the American Statistical Association (ASA), and was selected as a Kavli Fellow of the National Academy of Sciences. He is currently a Penn Fellow and serves as Editor-in-Chief of the journal BioData Mining.

Previous Webinars

Automating Machine Learning for Prevention Research
Presented by Jason Moore, Ph.D.
August 29, 2017
Use of the Electronic Medical Record in Prevention Research
Presented by William M. Vollmer, Ph.D.
June 15, 2017
Dissemination and Implementation Research: Challenges and Opportunities
Presented by Maria E. Fernandez, Ph.D.
April 20, 2017
Mixed Methods in Disease Prevention and Health Promotion Research
Presented by Leonard A. Jason, Ph.D.
March 27, 2017
Engaging in Qualitative Research Methods: Opportunities for Prevention and Health Promotion
Presented by LeConté J. Dill, Dr.P.H., M.P.H.
January 19, 2017
Series Topic #3: Overdiagnosis in Cancer Screening: Overcoming Challenges, Avoiding Mistakes
Presented by Ruth B. Etzioni, Ph.D.
November 18, 2016
Series Topic #2: Guidelines for Screening in Children
Presented by David C. Grossman, M.D., M.P.H.
October 20, 2016
Series Topic #1: Making Guidelines for Colon Cancer Screening: Evidence, Policy, and Politics
Presented by David F. Ransohoff, M.D.
September 27, 2016
The Opportunities and Challenges of Using Systematic Reviews To Summarize Knowledge About "What Works" in Disease Prevention and Health Promotion
Presented by Kay Dickersin, Ph.D.
July 25, 2016
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