AI Health Events

Our goal is to share learning experiences with a broad community, both at Duke and beyond. Our events include the AI Health Seminar Series, the monthly Spark Imaging Seminar Series, and workshops and studios throughout the year. You can sign up for our mailing list to receive emails about upcoming events.

Algorithm-Based Clinical Decision Support (ABCDS) Oversight: A framework for the governance and evaluation of algorithms to be deployed at Duke Health

Tuesday, February 14, 2023 12:00-1:00 PM (Eastern time)

Presented by:

  • Armando Bedoya, MD, MMCi; Duke Health Technology Solutions
  • Nicoleta J Economou, PhD; Duke AI Health

Register here: https://duke.zoom.us/webinar/register/WN_QivQyW8QTlOQm0NR5rwQYg

In 2021, Duke Health launched the Algorithm-Based Clinical Decision Support (ABCDS) Oversight program, a collaborative effort between the Duke University School of Medicine and the Duke University Health System to ensure high-quality care, patient safety, and ownership are maintained for algorithms and related tools. In this session, Drs. Economou & Bedoya will introduce you to the ABCDS Oversight framework. This session is open to members outside of Duke.

What are machine learning and artificial intelligence, and why do they matter in health?

Tuesday, February 28, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

  • Matthew Engelhard, MD, PhD; Assistant Professor of Biostatistics and Bioinformatics
  • Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health

Register here: https://duke.zoom.us/webinar/register/WN_oXcW126lRVaQHU9tCBb9BQ

The transformation in artificial intelligence (AI) and machine learning (ML) is impacting clinical medicine with potential for benefit but also harm. In this one-hour virtual seminar, we’ll introduce AI and ML, highlight capabilities of current methods for images, text, and other big data in clinical settings, and discuss considerations in how to use these powerful methods responsibly. This session will be a high-level introduction accessible to people with no prior knowledge of artificial intelligence.

What are informatics and data science, and why do they matter in health?

Tuesday, March 28, 2023 | 12:00 – 1:00 PM (Eastern time)

Presented by:

  • W. Edward Hammond, PhD; Professor in Family Medicine and Community Health, Research Professor in the School of Nursing, Professor of Biostatistics and Bioinformatics
  • Warren Kibbe, PhD; Professor in Biostatistics & Bioinformatics
  • Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health

Register here: https://duke.zoom.us/webinar/register/WN_j9WrGE3tSXOrqgaorKi59A

The disciplines of informatics and data science are critical to the use of technology in clinical research and healthcare. In this one-hour virtual seminar, we’ll discuss the foundations of these fields with 2 experts: Dr. Hammond, who is one of the great pioneers in medical informatics and a founding member of HL7®; and Dr. Kibbe, who is chief for Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics and Chief Data Officer for the Duke Cancer Institute. Our conversation will include what the COVID-19 global pandemic has taught us about challenges and opportunities in data infrastructure, a debate on the future and direction of the HL7® FHIR® standard, and a broad-ranging perspective into the evolution and future of informatics and data science.

Design and implementation of a novel platform for COVID-19 contact tracing and testing

Wednesday, April 12, 2023 | 4:00 – 5:00 PM (Eastern time)

Presented by:

  • Andrew Olson, MPP; Associate Director, Policy Strategy and Solutions for Health Data Science, Duke AI Health

Register here: https://duke.zoom.us/webinar/register/WN_oXddPm7bRXSJWzZI4aXDgQ

In July, 2020, just a few months into the COVID-19 pandemic, the CDC funded the Snowball study to test an approach for more efficiently identifying and contacting people in Durham, NC who may be at risk of infection. The study deployed respondent-driven sampling methods, in which individuals infected with COVID-19 serve as seed cases and recruit their own social contacts for testing and enrollment in the study. To operationalize this approach, we built a cloud-based platform that integrates with the Duke Health electronic health record for case ascertainment and with the Research Electronic Data Capture (REDCap) application for electronic consent and collection of a social contact survey. The platform generates unique coupons that participants validate to enroll in the study and that are used to track all of the links between seeds and multiple waves of peer participants. The Snowball study tested novel methods of contact tracing as well as conducting clinical research and in this seminar we will describe the design of the platform and share results from the study. This presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.

Past Events

Duke Electronic Health Records Study Design Workshop (EHR-SDW)

December 5-9, 2022

To learn more: https://aihealth.duke.edu/ehr-sdw-2022/

The Duke Electronic Health Records Study Design Workshop (EHR-SDW) will be offered in December as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding) as well as feedback on effective study designs that they will work on.

Duke Machine Learning Summer School

June 6–10, 2022

To learn more: https://aihealth.duke.edu/mlss2022/

The curriculum in the Duke Machine Learning Summer School (MLSS) is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLSS will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework.

This is the 11th Duke Machine Learning School presented since 2017. This series has reached hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. Last year’s machine summer learning summer school attracted 170 participants from around the world, representing 43 universities, institutes, and corporations.

 

AI Health Data Studio Seminars

Spring 2022

To learn more: https://aihealth.duke.edu/2022springdatastudios/

The AI Health Data Studio Seminars multi-part educational offering are designed for campus-based researchers at Duke who are interested in working with medical data but are unsure where to begin. The multi-part Data Studio seminar series will begin with an overview presented by AI Health Senior Informaticist Ursula Rogers, who has 25 years of experience in data management and software development. Additional individual sessions will feature data experts from across the Duke enterprise. Hosted by Ms. Rogers, Chief AI Health Scientist Ricardo Henao, PhD, and Associate Director of Informatics Shelley Rusincovitch, MMCi, the series builds on the successful AI Health Proposal Studios and extends structured opportunities for investigators to engage with Duke’s top data science expertise and thought leadership.