Posts – Page 2 – Duke AI Health

Duke AI Health Hosts December EHR Study Design Workshop

Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2022. 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 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. This workshop is offered through Duke AI Health’s Health Data Science (HDS) program and builds on the success of our highly successful Machine Learning Schools, with 11 events held since 2017.

AI Health Data Science Fellowship Program Welcomes New Members

The AI Health Data Science Fellowship Program is a two-year training program focused on data science with healthcare applications, designed for early-career data scientists with strong backgrounds in quantitative disciplines. Launched in fall of 2019, the program currently has 5 fellows, 2 staff data scientists, and 5 alumni. The program recently came together in-person for lunch for the first time since the pandemic. They gathered to welcome 2 new members: new fellow Angel Huang and new Data Scientist, John Rollman.

Image from the CAMELYON16 ISBI challenge on cancer metastasis detection: https://camelyon16.grand-challenge.org/Data/

AI Health Data Studio: Hands-On Digital Pathology

This in-person workshop presented by Ricardo Henao, PhD; Associate Professor, Department of Biostatistics and Bioinformatics; Chief AI Scientist, Duke AI Health, Akhil Ambekar, MS; Fellow, AI Health Data Science Fellowship Program, with Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health, will give you hands-on experience in working with medical digital pathology images using machine learning. Our use case will be in whole slide images of lymph node sections. We will use the CAMELYON16 dataset (https://camelyon16.grand-challenge.org/), which consists of 400 hematoxylin and eosin-stained whole-slide images. During the workshop, you will learn how to retrieve, manage, and process these images, then apply a machine learning model based on a neural network architecture to classify image regions as normal or malignant. The techniques you learn will also be broadly applicable to other types of medical imaging.

Chief AI Health Scientist Ricard Henao Named Associate Professor

Duke AI Health congratulates Chief AI Health Scientist Ricardo Henao, PhD, on his promotion to the rank of Associate Professor in the Department of Biostatistics and Bioinformatics in the Duke University School of Medicine. Dr. Henao is a major presence in health data science at Duke, where his leadership and expertise in machine learning methods and implementation have made him a sought-after collaborator and instructor. “Dr. Henao is a major asset to Duke AI Health and to the larger Duke community,” said Michael Pencina, PhD, director of Duke AI Health and vice dean for data science at the School of Medicine. “We feel fortunate to be able to benefit from such a rare combination of talent and knowledge spanning research, application, and teaching.”

Duke AI Health Director Michael Pencina Surpasses 100K Citations for Academic Research

Duke AI Health Director and Vice Dean for Data Science Michael J. Pencina, PhD, has achieved a major academic milestone: according to Google Scholar’s analytics, he has recently passed the 100,000 mark for academic citations of his work. Pencina, who in addition to his leadership role in Duke’s efforts to develop, evaluate, and implement ethical and equitable data science, has also worked extensively on the development and evaluation of risk prediction models and clinical trial designs.

Request for Comments: Coalition for Health AI’s White Paper on Bias, Equity, and Fairness

As a member of the Coalition for Health AI, Duke AI Health is working to develop a consensus-driven framework to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. The coalition is convening a series of virtual workgroup sessions to define core principles and has published a white paper from its first meeting: “Bias, Equity, and Fairness.” Please review the paper and submit your feedback by Sept. 15: https://bit.ly/3wbAXQx. With the help of your ideas, the Coalition for Health AI can advance towards establishing clear and appropriate guidelines and guardrails for the fair, ethical, and useful application of AI and machine learning in health care settings.

Portrait of Duke AI Health Director Michael Pencina, PhD

Much-Touted Genomic Test Score Shows Minimal Utility in Study Led by AI Health Director Michael Pencina

New research led by Duke AI Health Director Michael Pencina, PhD, published recently in the journal Circulation, looked at the value of using a genomic test to predict the future risk of heart disease. Pencina and colleagues found that the genomic test, referred to as the polygenic risk score (PRS), only marginally added to the predictive information obtained through the assessment of traditional risk factors, concluding that the PRS “had minimal clinical utility”.

Call for Student Applications: AI Health’s Fall 2022 HDS Research Program

We invite Duke students to apply for the Health Data Science (HDS) fall research program. This competitive program, based in Duke AI Health, is designed to allow students who have previous experience in data science to continue their engagement with substantive applied projects. The HDS Research Program offers Duke students, both undergraduate and graduate, the opportunity to be a part of research teams applying advanced machine learning (deep learning) to important areas of medicine. Participating students will be mentored by leading Duke faculty involved in data science research, often with guidance by practicing clinicians. The fall will culminate in a showcase session where student teams will present their results.

Duke Authors Introduce Framework for Clinical Algorithm Oversight

A group of Duke Health researchers recently shared their insights on approaches to managing the complex issues that are emerging as “algorithmic medicine” increasingly becomes part of clinical care at hospitals and health systems. The authors, who comprise faculty and staff from Duke AI Health, Duke Health Technology Solutions, the Duke Institute for Health Innovation, and other physicians and researchers from Duke University and Duke University Health System, published an account of their approach to evaluating and monitoring the use of algorithmic predictive models at Duke Health hospitals and clinics. The article, titled “A framework for the oversight and local deployment of safe and high-quality prediction models,” was published on May 31 in the Journal of the American Medical Informatics Association (JAMIA). It showcases the processes and procedures by which an expert group at Duke Health known as Algorithm-Based Clinical Decision Support (ABCDS) Oversight reviews, approves, and manages predictive models intended for use in patient care settings.

Duke AI Health Spark Seminar Series: Medical Imaging AI – Where do we go from here?

Can AI safely automate medical decision-making tasks to improve patient outcomes? In this talk, the presenters will share the challenges in the development and translation of medical AI, and how they are being addressed through a blend of innovation in algorithm development, dataset curation, and implementation design. They will first talk about self-supervised learning methods for medical image classification that leverage large unlabeled datasets to reduce the number of manual annotations required for expert-level performance. Then, they will discuss open benchmarks that can help the community transparently measure advancements in generalizability of algorithms to new geographies, patient populations, and clinical settings. Third, they will share insights from studies that investigate how to optimize human-AI collaboration in the context of clinical workflows and deployment settings. Altogether, this talk will cover key ways in which we can realize the potential of medical AI to make healthcare more accurate, efficient and accessible for patients worldwide.