Join a live training session for the N3C Data Enclave. Users of all types can learn how to navigate the N3C, utilize the Enclave and resources it provides, and learn how to better achieve their analytical goals. This orientation is split into 2 sessions – Session A and Session B. These orientations are offered on a monthly basis. Session A is recommended before attending Session B.

Session A is for those who want to learn about N3C, as well as how to engage with project teams and access the data.

Topics include:

• Provide a general overview of N3C, including goals, organization, and community resources such as Domain Teams and Data Liaisons.
• Introduces the 3 data tiers available and important considerations for research driven by the data harmonization process.
• Discusses resources for training and help, as well as the Data Use Request (DUR) process required for researcher access.

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This series is jointly sponsored by the UNC Core Center for Clinical Research (CCCR) and the UNC Program for Precision Medicine in Health Care (PPMH). All events will be virtual (Zoom) and free of charge.

The goal of this seminar series is to bring together researchers and clinicians across the UNC campus and catalyze new clinical research using machine learning. All backgrounds and experience levels will find this series engaging and informative.

Machine learning analysis methods offer the opportunity to integrate and learn from large amounts of biological, clinical, and environmental data, and there is a growing interest in how these tools can be used to inform and individualize clinical decision making in a variety of disease areas. Machine learning can offer different, yet often complementary, insights compared to traditional statistical analyses to better understand heterogeneity in patient presentation, prognoses, and treatment response, generating critical data for precision medicine research. These methods can allow integration across diverse data types and large feature sets, overcoming some limitations of traditional tools to answer clinical questions. However, many clinical researchers have little exposure to machine learning methods, presenting a barrier to utilization of these tools themselves and/or to effective collaboration with methodologists in their own research.

The objectives of this series are to:

On May 11, 2022, from 9:30-11:30 a.m., clinicians and researchers will discuss examples of how machine learning tools have been applied in arthritis and autoimmune disease. This session will feature an overview of machine learning and its application to identify clinical phenotypes of osteoarthritis and type 1 diabetes.

On May 18, 2022, from 1:00-3:00 p.m., clinicians and researchers will explore the use of machine learning tools and precision medicine techniques in clinical research. This session will feature an overview of machine learning tools in the field of precision medicine and address how they may be used to inform decision support for peripheral artery disease and rare genetic diseases.

On May 25, 2022, from 1:00-3:00 p.m., a panel discussion will focus on how researchers and clinicians at UNC can integrate machine learning techniques into their own clinical research. Are you a clinician with an idea for how patient care could be improved with computational decision support tools? Pitch your idea (5-10 minute overview) to assembled machine learning experts on May 25. Receive expert guidance and compete for funding from the UNC Program for Precision Medicine in Health Care for analytical support to develop your project. Email This email address is being protected from spambots. You need JavaScript enabled to view it. for more information about the pitch opportunity.

We encourage anyone interested in using machine learning as part of their own research to attend, regardless of research background or experience with machine learning!

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Ethics and Collateral Findings in Pragmatic Clinical Trials: Implications of a Multi-Method Exploration

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Jeremy Sugarman, MD, MPH, MA
Deputy Director for Medicine
Harvey M. Meyerhoff Professor of Bioethics and Medicine
Berman Institute of Bioethics
Johns Hopkins University

Stephanie Morain, PhD, MPH
Assistant Professor
Berman Institute of Bioethics
Johns Hopkins University

Juli Bollinger, MS
Research Scientist
Berman Institute of Bioethics
Johns Hopkins University

Kevin P. Weinfurt, PhD
Professor and Vice Chair of Research
Department of Population Health Sciences
Duke University School of Medicine

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