Duke AI Health Director Michael Pencina, PhD, who is a professor of biostatistics and bioinformatics at Duke and serves as the medical school’s vice dean for data science and information technology, was recently quoted in an article appearing in STAT News examining the use of commercially developed predictive algorithms in medicine. In an investigative report for STAT News, correspondent Casey Ross spoke with employees in multiple health systems across the country that use clinical algorithms created by Epic, one of the nation’s largest electronic health record vendors.
Duke+DataScience (+DS) is a Duke-wide educational initiative devoted to expanding knowledge of and facility with machine learning and other artificial intelligence tools across multiple academic fields, including the arts, humanities, and social sciences as well as medicine and quantitative sciences. With an extensive and growing curriculum that includes both online and in-person courses in neural networks, natural language processing, deep learning, and other machine learning applications, +DS offerings span learning needs ranging from novice to expert and are tailored to specific academic and professional applications.
Duke’s +Data Science (+DS) recently concluded its 2021 Machine Learning Virtual Summer School (MLvSS). This event, the ninth machine learning school held since 2017, sold out more than a month in advance and completely filled a 100-person waitlist. This high demand reflects both the substantial demand for instruction in methods driving the rapid growth in artificial intelligence, as well as a keen interest in tapping into high-quality instruction from Duke teachers with expertise in the mathematics and statistics that underlie modern machine learning methods.
Keeping up with the pace of research in health data science is challenging at the best of times, and the COVID-19 pandemic has not made things any easier. For this reason, Duke AI Health and the Duke +Data Science (+DS) program worked together this spring to launch the Proposal Studio Virtual Learning Experiences (vLE). The Proposal Studios sessions were designed to help investigators develop effective, successful proposals for research project involving health data science. From March through April of 2021, +DS held four successful proposal studios, assisting 13 investigators to develop scientific proposals. Open to anyone within the Duke community, the series attracted a total of 129 attendees and averaged 32 audience members per vLE.
The COVID-19 pandemic has prompted a surge in demand for telehealth services, but many questions about how healthcare providers can adapt their practice to meet the challenges of telemedicine remain to be answered. Now, a group of rheumatologists at Duke University School of Medicine have used data drawn from the Duke University Health System’s EHRs (electronic health records) to investigate how a rapid transition to telemedicine affected their approach to patient care.
A group of neuroscientists and machine learning experts are developing new ways to analyze animal movement and behavior to gain insights into the inner workings of the nervous system. Combining expertise from the disciplines of neurobiology and artificial intelligence, a team of researchers from Duke University, Harvard, MIT, Rockefeller University, and Columbia University have developed a system that captures detailed, multiple-view video of animals in their natural environment, and then uses data from those video images to build a detailed model of how the animal moves. This allows scientists to use movement and behavior as a window into brain function.
Ursula Rogers, senior informaticist with Duke Forge and AI Health, recently presented a poster at the American Medical Informatics Association (AMIA) 2021 Virtual Informatics Summit. The poster, “Enabling Data Liquidity for Health Data Science: A Suite of APIs for EHR Data” discusses an ongoing partnership between the Duke Health Technology Solutions (DHTS) Analytic Center of Excellence and AI Health. 18 application programming interfaces (API) have been developed to provide efficient and secure programmatic access to electronic health record (EHR) data for machine learning.
Since it was declared a global pandemic in March 2020, COVID-19 upturned university and college campuses across the United States, causing major disruption to student life. As Duke’s campus went into a full lockdown following a steep uptick in COVID-19 infections in North Carolina last spring, Duke’s Harshavardhan (Harsha) Srijay, a 19-year-old second-year undergrad student majoring in math and data science, saw his plans for the 2020 summer crumble. As prior opportunities fell through the cracks, the Duke Plus Data Science (+DS) Advanced Projects summer program provided him a platform to not only be engaged and productive through a very difficult summer, but also come out of it with a successful project that he recently presented at the American Medical Informatics Association (AMIA) 2021 Virtual Informatics Summit(link is external).
In this one-hour virtual learning experience, 3 teams of Duke investigators will discuss their proposal concepts with data science experts. For April 5, proposal concepts will include genomic analysis related to sickle cell anemia, lifestyle intervention adherence, and transplant optimization. The proposal studio vLE concept is newly launching in spring 2021, with the goal of assisting Duke investigators with proposal development in health data science, and in sharing experiences with the broader Duke community. The series is co-hosted by Duke AI Health and the Duke+Data Science (+DS) program.
AI Health is currently considering requests for placement of a Pathology AI Health Data Science Fellow. The AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. The Pathology AI Health fellow will be funded jointly by AI Health and the Department of Pathology. Fellows will also receive support from AI Health and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.