CRI Director Sam Volchenboum’s piece in Wired from last June, “Social networks may one day diagnose disease – but at a cost,” was named in TIME this week as one of the day’s “Five Best Ideas.” The best ideas are determined jointly by TIME and the Aspen Institute, an educational and policy studies organization based in Washington, DC. Read Sam’s article in Wired here.
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University of Chicago Medicine has been awarded a $50,000 grant from the Rally Foundation for Childhood Cancer Research to support CRI Director Sam Volchenboum’s work building a research data commons for rhabdomyosarcoma, a pediatric soft tissue cancer. This project, called INSTruCT, has the goal of bringing together clinical trials data from children with rhabdomyosarcoma around the world to enable data mining studies. Learn more about INSTruCT and the CRI’s other pediatric cancer research data commons work here.
The Department of Pediatrics has released a Request for Applications for a new initiative to foster excellence in research scholarship. This initiative will provide seed funding for promising clinical and health outcomes research projects focused on the health of children and their families. In particular, the Department aims to promote the early career development of translational and clinical faculty researchers, with the goal of generating preliminary data for subsequent funding applications.
This seed funding will be available to support services from the Center for Research Informatics, such as data requests from the Clinical Research Data Warehouse, data analysis by the Bioinformatics Core, or application development.
The awards: four awards of up to $5000 each
Who is eligible: faculty, fellows, and residents with faculty mentors
How to apply: find the Request for Applications here and apply by October 15
Phil Verhoef, MD, PhD of the Department of Medicine has been awarded an ITM Pilot Award for his research in treating sepsis, an infection that kills more than 250,000 Americans each year. Watch the video to learn about how Dr. Verhoef uses the Clinical Research Data Warehouse to study why and how allergies may protect people from sepsis and translate this knowledge into new treatments.
CTMS Business Owner and Executive Director of the Office for Clinical Research (OCR) Bethany Martell introduced the CTMS project to an audience of UChicago faculty and staff at the OCR Town Hall meeting last Friday.
OCR Town Hall meetings focus on issues relevant to clinical and translational research at the University and provide a venue for continual training and education. They are open to all faculty and staff.
As part of a newly announced five-year, $14.8 million NIH grant, the CRI will play a key role in building the Gabriella Miller Kids First pediatric data resource center. The center, a multi-institutional project headquartered at the Children’s Hospital of Philadelphia, will provide pediatric researchers with a much-needed way to work with large sets of genomic and clinical data related to childhood cancer in order to better predict and treat it.
Two UChicago data science groups, the CRI and the Center for Data Intensive Science (CDIS), will take part in the project under the leadership of CDIS Director Robert Grossman, PhD, and CRI Director Sam Volchenboum, MD, PhD. A team combining both groups’ expertise in building data commons will be responsible for designing and operating the technical foundations of the project: the software that will be used to process and share data within the center, including the integration of disparate data sources, coordination with third-party applications, and support for data analysis.
By leveraging our years of experience working with multi-institutional data networks and large biomedical datasets, through this project the CRI has the opportunity to make a major contribution toward ending childhood cancer. Read more about the Kids First center and UChicago’s role here.
The CRI-led Clinical Trials Management System project, which began in October of last year, will enter its source code development phase on Wednesday, August 9. This phase will be officially launched at a meeting attended by key stakeholders across the BSD and UCM. Updates throughout the development phase will be available on the CRI website. Learn more about this project and meet the team here.
The research collaboration between Google and University of Chicago Medicine was profiled this week in Becker’s Hospital Review, which called our partnership “a data-driven duo to watch.”
The article highlights how UCM will take advantage of the CRI’s Clinical Research Data Warehouse as a base for Google’s predictive models. CRI Director Sam Volchenboum explains why the CRDW is a particularly important resource for the Google project: “We’ve taken a very rigorous approach to our data warehouse that is not necessarily the norm. We’ve been able to take our clinical data and standardize it and clean it up in a way that makes it much more easy to analyze and to perform this type of machine learning.”
In Wired, CRI Director Sam Volchenboum shares a thought-provoking essay on the potential of social networks like Facebook and Google to predict disease. As our daily lives generate more and more trackable data through social media, wearables, GPS, etc., algorithms could become better and better at recognizing patterns that could point to health conditions — but the potential benefits of this are balanced with significant risks. Read Sam’s take on it here.
Google has announced announced a new partnership with University of Chicago Medicine which will take advantage of the CRI’s experience and expertise in working with biomedical data and predictive algorithms.
The Google team will work with University researchers to apply advanced machine learning techniques to de-identified patient data. These models may detect patterns that enable doctors to predict future health events — meaning they could anticipate patients’ needs before they arise, improve outcomes, and save lives. The partnership will expand on work in this area that is already in progress at UCM, such as the eCART model for predicting cardiac arrest.