This virtual seminar series will provide a background in the use of machine learning tools to answer clinical questions, understand the strengths and limitations of these methods, and examine real-world examples of machine learning methodology in clinical research. The series is co-sponsored by the UNC Core Center for Clinical Research and the UNC Program for Precision Medicine in Health Care.
The UNC Core Center for Clinical Research and the UNC Program for Precision Medicine in Health Care are co-sponsoring a series of virtual, free events on machine learning tools for clinical researchers. Anyone interested in using machine learning as part of their own research is encouraged to attend, regardless of research background or experience with machine learning. The goal of the seminar series is to bring together researchers and clinicians across the UNC campus and catalyze new clinical research using machine learning.
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 event series will series provide a background/foundation of knowledge regarding the use of machine learning tools in clinical questions, help attendees understand the strengths and limitations of these methods, help attendees recognize some real-world examples of applied machine learning methodology in clinical research, and elucidate how machine learning can be used to advance precision medicine research.
On May 11, 2022, 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. Register online to attend.
|May 11 agenda
Machine Learning Tools & Precision Medicine in Arthritis & Autoimmunity
|9:30am||Machine learning didactic overview (unsupervised vs supervised methods, advantages, limitations, requisite data requirements) (Daniel de Marchi)|
|10:00am||Type 1 Diabetes phenotypes (Anna Kahkoska)|
|10:30am||Osteoarthritis phenotypes (Amanda Nelson and Tom Keefe)|
|11:00am||Q&A/panel questions and discussion|
On May 18, 2022, 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. Register online to attend.
|May 18 agenda
Machine Learning Tools & Precision Medicine in Clinical Research
|1:00pm||Precision medicine overview and SMART trial designs and how those can be used with ML methods to infer precision medicine decision support (John Sperger)|
|1:30pm||Precision medicine machine learning analytics to improve PAD treatment decisions and outcomes (Katharine McGinigle and Nikki Freeman)|
|2:00pm||Machine learning in Epic precision medicine machine learning analytics to address diagnostic odysseys among pediatric patients with rare genetic diseases (Michael Adams and Kushal Shah)|
|2:30pm||Q&A/panel questions and discussion|
On May 25, 2022, 1 – 3 p.m., a panel discussion will focus on how researchers and clinicians at UNC-Chapel Hill can integrate machine learning techniques into their own clinical research. Register online to attend.
Clinicians with ideas for how patient care could be improved with computational decision support tools can pitch their idea (5-10 minute overview) to assembled machine learning experts during the May 25 session. Attendees also will receive expert guidance and can compete for funding from the UNC Program for Precision Medicine in Health Care for analytical support to develop their projects. Participants can email email@example.com for more information about the pitch opportunity.