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.
Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2024
Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2024. The workshop 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 e-data from electronic health records (EHRs). The EHR-SDW will introduce learners to the components of EHR data and in considerations for designing 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.
This course will be conducted virtually via Zoom.
To register for the EHR-SDW, please visit https://events.duke.edu/ehr-sdw-2024
To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_a3Fs9TyyK82Bkr4
The deadline for registration is Friday, November 22, 2024.
Past Events
Recently completed seminars and events
AI Health Virtual Showcase: Research Applications with Harmonized Variables from the Framingham, MESA, ARIC, and REGARDS Studies
Wednesday, November 6th, 2024 | 12:00PM – 1:00PM (Eastern time)
Presented by:
- Chuan Hong, PhD; Assistant Professor of Biostatistics & Bioinformatics, Duke University School of Medicine
- Pratheek Mallya, MS; Product Development Manager, Data Science; American Heart Association
- Matt Engelhard PhD, MD; Assistant Professor of Biostatistics & Bioinformatics; Duke University School of Medicin
Moderated by:
- Michael Pencina, PhD; Vice Dean for Data Science, Duke University School of Medicine, Chief Data Scientist for Duke Health, and Director of Duke AI Health
- Jennifer Hall, PhD, FAHA; Chief of Data Science and Co-Director of the Institute for Precision Cardiovascular Medicine; American Heart Association
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b3b216b1-19c7-4ea5-afce-b22100f54e51
Research in stroke risk prediction and prevention is enhanced by the inclusion of a broad range of data from different patient cohorts. Integrating and harmonizing multiple data sources increases generalizability, sample size, and representation of understudied populations—strengthening the evidence for the scientific questions being addressed. In an AI Health Virtual Seminar presented earlier this year, researchers from Duke AI Health and the American Heart Association (AHA) shared the open metadata repository they developed for the harmonization of stroke risk prediction variables from four large, National Institutes of Health (NIH)-funded cohort studies: REGARDS (Reasons for Geographic and Racial Differences in Stroke), FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities).
In this follow-up seminar, leading researchers from Duke AI Health and AHA will present new methodologies and results from studies that were conducted with the harmonized dataset. Chuan Hong, Assistant Professor of Biostatistics & Bioinformatics; Duke University School of Medicine, will present a learning network for cohort-to-EHR variable harmonization based on semantic learning. Pratheek Mallya, Product Development Manager, Data Science; American Heart Association, will introduce a technique using natural language processing (NLP) models to automatically harmonize and standardize variable descriptions from three different stroke data cohorts and compare the performance of the proposed method with a baseline logistic regression model. Matt Engelhard, Assistant Professor of Biostatistics & Bioinformatics; Duke University School of Medicine, will present an AI model for stroke risk prediction designed to make predictions more similar
Building a platform for discovery: the ARDS, Pneumonia and Sepsis Phenotyping Consortium
Thursday, October 24, 2024 | 12:00 PM – 1:00 PM (Eastern Time)
Virtual seminar via Zoom, open to members internal and external to Duke
Presented by:
- Chris Lindsell, PhD, Professor of Biostatistics & Bioinformatics
- Christina Barkauskas, MD, Associate Professor of Medicine
- Anru Zhang, PhD, Associate Professor of Biostatistics & Bioinformatics
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=0587810c-d844-43de-9bae-b2130124c07a
View the slides here:
https://duke.box.com/s/nrejkmirq8bfikv2ae4f6kuqwa5kjsnv
ARDS, pneumonia, and sepsis are common critical illnesses with high mortality. The conditions are overlapping, and diagnosis, prognosis and prediction are all challenging. Decades of research has been done to try and unravel the heterogeneity and find treatments to improve outcomes for these patients, yet to little effect. Progress is hampered in part by the high acuity and time-sensitive clinical context, the plethora of interventions required for organ support, limited understanding of the pathophysiology, incomplete assessment of molecular data, and incomplete documentation of clinical course – among other things. To fill the knowledge gap and provide the foundation for successful interventions in these high mortality conditions, the APS consortium is generating the most comprehensive dataset ever to be available. This talk will discuss the challenges and solutions in designing and implementing this ambitious prospective observational cohort study that will deeply phenotype 4000 patients with serial assessments throughout their clinical course. We will also discuss the principles being applied to maximize the validity of analyses, and we will highlight opportunities for ancillary studies.
Duke Summit on AI Innovation in Health
October 9-11, 2024
Duke AI Health and the Pratt School of Engineering are partnering to host the Duke Summit on AI for Health Innovation with the goal of fostering a community of practice around health-oriented AI development, that bridges the medical and engineering fields.
This event will leverage the strengths of Duke in AI product development and the ecosystem for healthcare innovation, harnessing responsible AI in the service of patients and communities for better health. To enhance the impact of our discussions and collaborations, the summit will integrate the principles of design thinking into its programming. This approach will guide participants through a process of empathy, ideation, prototyping, and testing, ensuring that solutions are user-centered and more readily deployed once developed.
Learn more at https://aihealth.duke.edu/2024-innovation-summit/
Registration is open to everyone, including faculty, students, and staff at Duke and other academic institutions, as well as healthcare professionals, industry professionals, and business professionals. Please join us!
Introduction to Basic Concepts in Machine Learning
Tuesday, September 10, 2024 | 12:00 PM – 1:00 PM (Eastern Time)
Virtual seminar via Zoom, open to members internal and external to Duke
Presented by:
Ricardo Henao, PhD
Associate Professor of Biostatistics & Bioinformatics
Director of Clinical Trials AI, Duke Clinical Research Institute
View the recording here: https://duke.zoom.us/webinar/register/WN_cXJVa4-aS0GgN9wsdlhLyg
Dr. Ricardo Henao will introduces the basic concepts of machine learning with a focus on intuition and examples. The simpler and widely used logistic regression model are introduced first, and from this, the multilayered neural network will be introduced as a generalization. Concepts of parameter learning (optimization), generalization (and overfitting), validation and performance evaluation are also introduced.
This was Dr. Henao’s first seminar since returning to Duke on July 1st after a leave of absence at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, Dr. Henao has returned to North Carolina as Associate Director of Clinical Trials AI for the Duke Clinical Research Institute (DCRI) and as a member of AI Health’s Faculty Council. Dr. Henao is Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine.
From Silos to Insights: a Data Strategy for Integrating Clinical Registries
Thursday, September 26, 2024 | 12:00 – 1:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
Presented by:
Anthony Sorrentino, MD, Clinical Informatics Fellow at Duke University Health System and Primary Care Doctor at Duke Primary Care Riverview
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=427b0370-bd73-4ef9-a40b-b1f7011e434d
Real world data, such as that from medical claims, the electronic health record, or clinical registries, are becoming increasingly important in quality improvement and research. Of these, clinical registries are unique in containing clinical outcomes data that are manually abstracted from the unstructured portions of the EHR by trained registrars or clinicians. This makes them a rich source of high-quality, clinical outcomes data with broad secondary use cases including comparative effectiveness research, cohort generation, and model development. However, use of clinical registry data is limited by idiosyncrasies in data storage, access, and governance – resulting in siloed datasets that are cut off from the broader health data landscape. Our work aims to unlock the potential of patient registries by creating a pilot data mart that links several of Duke’s clinical registries with existing EHR data. In this session, I will give an overview of Duke’s strategy for creating this data mart. I will highlight unique challenges we faced, and design considerations employed to address them.
Virtual Symposium on AI Presented by DAISI (Duke NUS AI + Medical Sciences Initiative)
Thursday, June 6, 2024 | 8-10 AM (EDT / 8-10 PM (SGT)
Open to all affiliated with Duke and Duke-NUS
View the recording here: https://duke.zoom.us/rec/share/YAhPQmUMkrWEsVt_y8XafGIhvjT7xFG2sBjVhOi4RjaJACTnEADGYh9vskJnQq3d.0UPwWTSYE5JKW0WL
View the project handbook here: https://duke.is/aihealthprojects
Duke University and Duke-NUS Medical School in Singapore’s AI + Medical Sciences Initiative (DAISI) invite you to a virtual symposium on AI, in partnership with the Duke AI Health Community of Practice.
This two-hour event on Thursday, June 6 from 8am – 10am (EDT) will feature lightning talks — short, fast-paced presentations — presented by faculty and staff involved in health-related topics.
Facilitating Harmonization of Variables from the Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository
Tuesday, May 21, 2024 | 12:00 PM – 1:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3859a4a5-c1c4-4ac1-b75b-b17800e11c58
Link to the Metadata Repository on the AHA Precision Medicine Platform: https://precision.heart.org/duke-ninds
Presented by:
- Pratheek Mallya, MS; Product Development Manager, Data Science; American Heart Association
- Chuan Hong, PhD; Assistant Professor of Biostatistics & Bioinformatics, Duke University School of Medicine
Moderated by:
- Andrew Olson, MPP, Associate Director, Policy Strategy and Solutions for Health Data Science
Research in stroke prevention requires inclusion of a broad range of data sets from different cohorts. Integrating and harmonizing different data sources are essential to increase generalizability, sample size, and representation of understudied populations—strengtheningthe evidence for the scientific questions being addressed. To that end, Duke AI Health and the American Heart Association have developed an open metadata repository for the harmonization of stroke risk prediction variables from four large, National Institutes of Health (NIH)–funded cohort studies: REGARDS (Reasons for Geographic and Racial Differences in Stroke), FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities). In this webinar, we will present an overview of the metadata repository and walk through its features, including variable distributions, collection time periods, and search filters. We will also discuss several use cases for incorporating this resource into your research for harmonizing data.
Ushering in the Algorithmic Century: Duke’s Pathfinding Leadership in Ensuring that Health AI Is Safe, Effective & Equitable for All
Tuesday, April 30, 2024 | 12:00 PM – 1:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=263972bb-1e75-4297-bc66-b162012dcb6f
Presented by:
In this seminar, Dr. Michael Pencina, Vice Dean for Data Science, Chief Data Scientist for Duke Health, and Director of Duke AI Health, will examine the ongoing revolution in health data science and the application of artificial intelligence tools to the field of medicine. He will trace the evolution of increasingly sophisticated AI-powered technologies now being leveraged for patient care and clinical research, surveying the challenges and opportunities presented by these algorithmic technologies. In particular, the seminar will focus on the increasingly urgent need for robust oversight of health AI tools that encompass the entire lifecycle of such technologies, from development through validation and deployment. He will explain how robust frameworks of review and continuous oversight – including ones being articulated by Duke and its partners – are critical to ensuring that current and future applications of AI in healthcare and clinical research are safe, accurate, trustworthy, and equitable.
This seminar will emphasize Duke University’s contributions to a field that is rapidly reshaping the practice of healthcare and biomedical science for the coming century. The presentation will touch on the history of applied healthdata science at Duke, understood as part of a tradition of innovation that dates back to the pioneering efforts of Duke Cardiology professor Eugene Stead in creating computer-based predictive tools for improving patient care. In addition, the seminar will also acknowledge the larger legacy of innovation and collaboration between the disciplines of medicine and computer science at the university. It will also explore how Duke continues to play a major role in the AI revolution as it transforms medicine and clinical research in the coming century, with particular emphasis on developing frameworks of governance, review, and oversight that ensure that health AI technologies are safe, trustworthy, and equitable.
Machine Learning for Precise Diagnostics and Therapeutics
Presented by: Rohit Singh, Assistant Professor of Biostatistics & Bioinformatics with host Aditya Parekh
Drug discoveries have been instrumental in improving global health over the last century, but the median drug now takes about 10 years to bring to market and costs over a billion dollars to develop. The work in our lab aims to expedite the development of precise diagnostics and therapeutics by applying machine learning. In this talk, I will outline two recent research directions. In the first part, we use single-cell multiomics to discover regulatory mechanisms governing the interaction between the epigenome, transcription factors, and target genes. In the second part, I will introduce the application of large language models to model protein interaction and function. These protein language models enable powerful new approaches to predicting and understanding protein-protein and protein-drug interactions. I will conclude with a prospective look at how similar methods may help answer foundational questions in both basic and translational science.
Centennial Summit on Data Science in Surgery
April 18, 2024, 8:00 AM – 1:00 PM | In person at Trent Semans Center for Health Education, 6th Floor
Register here: https://duke.qualtrics.com/jfe/form/SV_abLQdJa7FI81G4e
The Department of Surgery will celebrate Duke’s Centennial with the Centennial Summit on Data Science in Surgery, featuring speakers from Duke Surgery and Duke AI Health, to examine the future of data science and innovation in surgical application. The keynote address will be given by Ozanan Meireles, Vice Chair for Innovation in the Department of Surgery and Director of Surgery for Duke AI Health. AI Health’s Director Michael Pencina will also be a featured presenter.
AI Health Virtual Seminar: Myth vs. Reality: Unraveling common misconceptions about heat-related illnesses
Thursday, April 18, 2024 | 12:00 PM – 1:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=06e91f16-9ab9-4a34-9e89-b15a0116b3bb
Follow along with the slides here: https://duke.box.com/s/gei22rh16pxt71eq9ng4kw1lx4zzz74a
Presented by:
Ashley Ward, PhD
Director, Heat Policy Innovation Hub
Nicholas Institute for Energy, Environment & Sustainability
with moderator Jennifer M. Lawson, MD, MA, Assistant Professor of Pediatrics and Faculty Associate of the Trent Center for Bioethics, Humanities and History of Medicine at Duke University
Summer of 2023 served as a wakeup call for many regarding the impact of extreme heat. The toll on communities was severe. In regions of the US, planes were grounded, emergency departments and hospitals were at capacity, industry operations were interrupted, and the military cancelled training exercises. While it is not a secret, it is also not a well-known fact that heat kills more people in the US than any other weather-related event, and its impact on the health of the most vulnerable is severe. Moreover, as temperatures continue to rise, so do the risks associated with heat-related illnesses, making it crucial for health providers to stay informed and equipped with accurate knowledge. In this session, we will discuss extreme heat and its impact on public health, focusing on the prevalent myths and misconceptions surrounding extreme heat, shedding light on the realities that healthcare providers need to understand to effectively mitigate risks and provide optimal care.
AI Health Virtual Seminar Series: Evaluating Generative Large Language Models in Healthcare
Tuesday, April 16, 2024 | 12:00 PM – 1:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=c927af71-1560-4181-b0db-b154012694bd
Presented by:
Chuan Hong, PhD; Assistant Professor of Biostatistics & Bioinformatics, Duke University School of Medicine
The rapid evolution of large language models (LLMs) has ushered in a new era of computational linguistics, yet a systematic approach to their evaluation, particularly in sensitive domains such as healthcare, remains nascent. This work bridges these gaps by offering a detailed and integrated review of qualitative evaluation, quantitative evaluation, and meta-evaluation. For quantitative evaluation, our review introduces a taxonomy of evaluation metrics, categorizing them based on essential dimensions such as human supervision, contextual data, and analytical depth. In addition to generic settings, our work distinctively emphasizes additional considerations vital in the healthcare sector. As a result, we propose an integrated cross-walk between qualitative and quantitative assessment methods. The proposed framework harmonizes qualitative insights, such as user-focused evaluations, with objective quantitative metrics. We present a detailed “go-to menu” of evaluation criteria, tailored to address specific healthcare applications and emphasize distinct aspects in both pre-deployment and post-deployment phases. Our findings underscore the need for evaluations that extend beyond mere technical accuracy, factoring in medical ethics, fairness, equity, and potential operational biases. Our work offers a summary of existing methods of LLM evaluation that can establish a baseline from which future evaluation methods can be developed to keep pace with the rapid advancements in the field.
AI Health Virtual Seminar Series: Using artificial intelligence to automate the screening of psychiatric distress in ophthalmology clinics using electronic health records
Thursday, April 11, 2024 | 4:00 PM – 5:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=45c35073-afbc-4d45-b336-b150015b43a9
Presented by:
Samuel Berchuck Ph.D. Assistant Professor of Biostatistics & Bioinformatics, with host Ayush Jain
In patients with ophthalmic disorders, psychosocial risk factors play an important role in morbidity and mortality. Proper and early psychiatric screening can result in prompt intervention and mitigate its impact. Because screening is resource intensive, we developed a artificial intelligence (AI) infused framework for automating screening using electronic health record (EHR) data. EHR data in our study came from the Duke Ophthalmic Registry, a retrospective EHR database that contains medical and clinical records for all patients seen at the Duke University Eye Center since 1993. Our study demonstrates that prescreening for distress in ophthalmology patients can be automated using an AI algorithm trained on existing EHR data. The algorithm identified distress in patients already being treated, and in those with incident distress. These findings suggest that screening for distress in ophthalmology clinics is feasible and may reduce negative health outcomes in patients.
Duke Symposium on Fostering AI/ML Research for Health Equity and Learning Transformation (FAIR HEALTH™)
March 13-14, 2024 | In person at the Duke University School of Nursing
We are thrilled to invite you to our upcoming symposium, Fostering AI/ML Research for Health Equity and Learning Transformation (FAIR HEALTH™), scheduled for March 13-14, 2024, at the Duke University School of Nursing in Durham, NC. This two-day event is dedicated to advancing discussions on cutting-edge research and practices aimed at promoting equity and fairness in algorithmic systems.
AI Health Hands-on Workshop: Using Large Language Models (LLMs) while measuring their accuracy and reacting to improve quality
Friday, January 26, 2024 | 11am – 2pm EST
In person in CRTP 2 in Hock Plaza, Live virtual streaming also available
Registration is open to everyone, including both academia and industry partners
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=74b7575d-7dc0-49a2-b6d9-b1030165887b
You can view the slides here: https://docs.google.com/presentation/d/1_XKxJzapiImVpTUr6wdR5tZXOrA9dXR06NUthZO_nFg/edit#slide=id.g2b38dd3077e_0_0
Presented by:
Sage Arbor, PhD., Sr. Informaticist,
Technology & Data Solutions, Duke Clinical Research Institute
Please join Sage Arbor, PhD. for a dynamic and collaborative learning experience where participants will delve deep into the world of large language models. Attendees will engage in hands-on activities ranging from prompt engineering techniques, investigating how AIs can improve other AIs, exploring the quality of model outputs, and making insightful comparisons between different language models.
Embark on a journey to understand the nuances of quality metrics, distinguishing between those that are easily measurable and those that pose challenges. Explore the multifaceted aspects of evaluating AI-generated content, emphasizing the importance of human evaluation in refining model performance.
One of the highlights of the workshop is the discussion and exploration of various strategies to enhance AI models. AIs with personas of quality assurance inspectors which cycle to improve another AIs output will be explored. Attendees have the opportunity to gain practical insights into optimizing and customizing models to better suit specific tasks and domains, thus appreciating the potential for tailoring AI to their unique needs.
In this intellectually stimulating workshop, participants not only gain a deeper understanding of large language models but also acquire the skills and knowledge necessary to harness the full potential of AI in their respective fields.
Artificial intelligence and the ethics of use: Patient and provider perspectives on utilizing prediction models in medical decision-making
Wednesday, January 17, 2024 | 4:00 PM – 5:00 PM (Eastern time)
Virtual seminar via Zoom, open to members internal and external to Duke
View the recording here: https://duke.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=feb399a5-8641-4e36-a08b-b0fb0130d465
Presented by:
Jessica Sperling (Smokoski), Ph.D.
Social Science Research Institute (SSRI)
Clinical & Translational Science Institute (CTSI)
Clinical predictions are a critical component of delivering quality healthcare. As clinical data grows rapidly in volume and quality, a large amount of research has been dedicated to developing machine learning (ML)-based clinical predictive models (CPMs), which can leverage large amounts of patient information to predict life expectancy, disease progression, and other health outcomes. In conjunction with attention to these models’ development, we must also consider ethics of how these models can be best utilized and understood by patients and providers, including considering interpretability and implications for medical decision-making. In this seminar, we discuss to a current qualitative study examining these factors as understood by four key end-user groups: clinical providers; support providers such as dialysis staff and social workers; patients; and patients’ caregivers (e.g., family members). As a use case, this study specifically examines a mortality prediction model for patients undergoing hemodialysis. We identify key factors related to trustworthiness, interpretability and use, and we provide suggestions of use-focused considerations to examine and prioritize ongoing.
FAIR HEALTH (Fostering AI/ML Research for Health Equity and Learning Transformation) ™
January 8, 2024, 9:00 AM – 12:00 PM
In person at Sarah Duke Gardens in Kirby Horton Hall
Register to reserve your spot: https://duke.qualtrics.com/jfe/form/SV_6sCG5CMTCryDuGG
This event is open to anyone who is passionate about advancing healthcare through innovation while ensuring health equity and fairness in clinical algorithms.
This workshop will delve into the critical issue of algorithmic bias and clinical decision making. This is an opportunity to gain essential insights and practical strategies to identify, mitigate, and evaluate bias in clinical algorithms. We will also explore the legal and ethical implications of algorithmic bias in healthcare, an aspect that is gaining increasing importance in today’s dynamic healthcare landscape. To enrich the workshop and encourage active participation, we have incorporated a combination of lectures and an interactive case study discussion, making it an even more rewarding experience for all attendees. By engaging in this workshop, and learning from one another, we can pave the way for a future where healthcare algorithms enhance patient care, minimize bias, and prioritize equity.
