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Machine Learning Tools for Clinical Researchers: A Pragmatic Approach Series

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 tools can facilitate integration of diverse data types and large data sets.

This series introduces clinical researchers to the use of machine learning tools in clinical research and provides real-world examples of their applications.

May 11, 9:30-11:30am
Machine Learning Tools & Precision Medicine in Arthritis & Autoimmunity

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. In this session, 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.

May 18, 1:00-3:00pm
Machine Learning Tools & Precision Medicine in Clinical Research

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. In this session, 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.

May 25, 1:00-3:00pm
Integrating Machine Learning into Clinical Research & Health Care

Machine learning analysis methods offer the opportunity to integrate and learn from large amounts of biological, clinical, and environmental data. 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 or researcher 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. Receive expert guidance and compete for funding from the UNC Program for Precision Medicine in Health Care for analytical support to develop your project.

Register separately for each event in the series.

Click here for more information and to register.

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