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Now Accepting Proposals for Placement of a Microsoft–Duke AI Health Fellow for Projects within the School of Medicine

AI Health is currently considering requests for placement of a Microsoft-Duke AI Health Data Science Fellow for projects proposed by Departments/Divisions within the Duke University School of Medicine. The Microsoft–Duke AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. Funded in part by a grant from the Microsoft Corporation, Microsoft-Duke AI Health Fellows will also receive support from AI Health, the clinical divisions to whose projects they are assigned, and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.

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Call for Applications: The AI Health Proposal Studios | November 11, 2020

The mission of Duke AI Health is to enable discovery, development, and  implementation of artificial intelligence (AI) at Duke and beyond. A key  component to achieving this goal is to foster high-impact, rigorous, and  competitive proposals for scientific awards. The AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. The deadline for submitting applications is 5:00 PM Eastern time on Monday, December 7, 2020.

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Machine Learning for Mobile Health Workshop Invites Abstracts

A Machine Learning for Mobile Health workshop, part of the upcoming Neural Information Processing Systems Conference (NeurIPS 2020), is inviting contributions and extended abstracts from researchers and clinicians in the interdisciplinary machine learning and mobile health space, with the goal to better address the various challenges currently facing the widespread use of mobile health technologies in health and healthcare. Co-organized by Duke Statistical Science assistant professor Katherine Heller, PhD who is also a research scientist at Google AI, the workshop aims to facilitate collaboration between machine learning researchers, statisticians, mobile sensing researchers, human-computer interaction researchers, and clinicians from around the world.

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Duke +DS Upcoming Virtual Learning Experiences (vLEs)

Seven Duke +DS learning experiences will be held in September. These sessions offer the opportunity to dive deeper into topics and target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary.

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Duke DataFest Analysis Supports Effectiveness of Social Distancing in Reducing the Spread of COVID-19

Across the world and in the United States, multiple studies have shown that social distancing is effective at reducing the spread of SARS-CoV-2 both at interpersonal and statewide levels. An early analysis of social distancing in the United States amid the COVID-19 pandemic, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge by Duke undergraduates Shannon Houser and Jack Lichtenstein, echoed those findings and won the “Best Visualizations” prize at the contest. Using data available from Google Mobility Reports, the duo explored how factors such as population density, initial number of positive coronavirus cases per capita, governor’s political affiliation, and official shelter-in-place orders influenced the magnitude of a state’s social distancing early during the COVID-19 pandemic.

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Duke DataFest Analysis Reveals How COVID-19 Impacts Communities Already Suffering from Health Disparities

Aside from altering the very fabric of daily life across the United States and the world, the COVID-19 pandemic has exposed the many existing shortcomings and inequities of the American healthcare system. The burgeoning public health crisis has resulted in more than 5 million confirmed cases nationwide and close to 163,000 deaths as of the beginning of August. However, some communities and groups have been disproportionately impacted, as a prize-winning analysis by Duke’s Meredith Brown, Matt Feder, and Pouya Mohammadi, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge.

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Duke Hosts Symposium on Addressing Public Health Crises with Data Science

The Duke University School of Medicine’s Office of Data Science and Information Technology and Duke AI Health partnered to co-host a virtual symposium on Wednesday, June 24, focused on using data science to combat public health crises. In her opening remarks, School of Medicine Dean Mary E. Klotman said that we are in the midst of two pandemics—COVID-19 and racism. The School of Medicine, of which the DCRI is part, will play an active role in driving solutions to both of these pandemics, and data science is one of the key tools that will be used. The symposium, titled “Public Health Crises of 2020: Battling COVID-19 and Disparities with Data,” featured seven DCRI faculty and data science experts from a range of other Duke entities such as Duke Forge and AI Health. The event also featured speakers external to the University, including two keynote speakers from the NC Department of Health and Human Services, as well as speakers from Change Healthcare and Amazon Web Services Data Exchange. The event, which was delivered in rapid-fire five-minute talks, was hosted and moderated by the DCRI’s Michael Pencina, PhD, Vice Dean for Data Science & Information Technology (pictured left). Other DCRI speakers included Jessilyn Dunn, PhD; Benjamin Goldstein, PhD; Ricardo Henao, PhD; Keith Marsolo, PhD; Susanna Naggie, MD; and DCRI fellow Jedrek Wosik, MD.

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Winners Announced for Duke ASA DataFest COVID-19 Virtual Data Challenge

A flyer for ASA Datafest

Creativity and insight were on display as a panel of judges announced the winners of the Duke ASA DataFest: COVID-19 Virtual Data Challenge on May 5th. The contest, which took place from April 8th through April 22nd, encouraged Duke students to use data science to explore unique effects of the COVID-19 pandemic on daily life and different aspects of the social fabric of the United States. Contest participants, working alone or in teams, were prompted to use publicly available data resources to gain insights into the cultural and societal impact of the global COVID-19 pandemic. The entries were judged by a panel of 15 experts drawn from academia and industry, with prizes awarded in categories that included “Most creative topic or data set”; “Best Visualizations”; “Best Interactive Dashboard”; “Best Insight”; and a “Judges’ Pick” award to recognize achievement outside of the other categories.

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A Primer on Biodefense Data Science and Technology for Pandemic Preparedness

Photograph of an art installation comprising colorful open umbrellas hanging overhead. Image credit: Inset Agency via Unsplash

BLOG: “During the onset of an event such as the one we’re now experiencing, resilience is the key priority. Secure your systems and protect your family and business. Remember, cybercrime spreads just as easily from personal devices to work devices as viruses do between people. Biodefense may have previously been considered the domain of the military and antiterrorism experts, but all of us now have a potential role to play. Please consider lending your time and expertise.” – Eric D. Perakslis, PhD

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Learning by Doing with Electronic Health Record Data

Several people sit on a white floor while enveloped in a complex webwork as part of an art exhibit. Image credit: Alina Grubnyak via Unsplash.

BLOG: “As we advance into the era of learning health systems, we need to systematize a process for how clinicians and data scientists can work together to solve important problems with EHR data.” – Andrew Olson, MPP, and Scott Kollins, PhD

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