The 2015 Data Science Bowl challenged data scientists to create an algorithm to automate a heart function assessment process. The National Institutes of Health and Children’s National Medical Center compiled data from more than 1,000 patients that participants examined. This data set was an order of magnitude larger than any previously released data set of its kind. With it came the opportunity for the data science community to take action to transform how we diagnose heart disease. Stay tuned for the results from this challenge.
We All Have a Heart
Although we often take it for granted, it’s our heart that gives us the moments in life to imagine, create, and discover. Yet cardiovascular disease threatens to take away these moments. Each day, 1,500 people in the U.S. alone are diagnosed with heart failure—but together, we can help. We can use data science to transform how we diagnose heart disease. By putting data science to work in the cardiology field, we can empower doctors to help people live longer and spend more time with those that they love.
Measuring Heart Function
Declining cardiac function is a key indicator of heart disease. Doctors determine cardiac function by measuring the heart’s squeezing ability. The gold standard test to accurately make this assessment uses Magnetic Resonance Imaging (MRI), but reading MRI images is a manual and slow process. It can take a skilled cardiologist up to 20 minutes to read these images—time the cardiologist could be spending with his or her patients. Making this measurement process more efficient will enhance doctors’ ability to diagnose heart conditions early, and carries broad implications for advancing the science of heart disease treatment. By revolutionizing the process of diagnosing heart disease, we can give doctors their best opportunity to proactively create robust treatment plans for stopping this silent killer.
The top prize was awarded to Tencia Lee and Qi Liu, a team with a background in hedge fund trading and not traditional data scientists. They spent more than 100 hours each in evenings and on weekends building and testing algorithms. Working in parallel, Lee and Liu built and trialled hundreds of algorithms to read the heart scans. Their efforts paid off, with the largest prize in the competition, among 993 data scientist contestants in the Data Science Bowl.
View the Public Leaderboard for other top-ranked entries from the 2015-2016 Data Science Bowl.
The National Institutes of Health
Michael S. Hansen, PhD
Michael is a biomedical engineer with a PhD from University of Aarhus, Denmark. He works at the National Heart, Lung, and Blood Institute (NHLBI), where he focuses on fast MRI techniques for real-time imaging and interventional procedures. His particular areas of interest are fast pulse sequences, non-Cartesian imaging, real-time reconstruction, GPU based reconstruction, and motion correction. Michael obtained his PhD from Aarhus University, Denmark, on the topic of fast dynamic imaging with special focus on cardiac imaging and image reconstruction. During his training, he spent time at the ETH in Zurich, Philips Research in Hamburg, King’s College London, University College of London, and Great Ormond Street Hospital for Children in London. Before coming to the NIH, he also worked as a laboratory head at Novartis Institutes for BioMedical Research in Cambridge, MA.
Andrew E. Arai, MD
Andrew joined the National Heart, Lung, and Blood Institute (NHLBI) in 1993. He is a Senior Investigator in the Laboratory of Cardiac Energetics in the Institute’s Division of Intramural Research. Andrew studies conditions and diseases that alter the heart’s supply and utilization of energy. His expertise is in the use of magnetic resonance imaging (MRI) to evaluate patients with heart attacks and coronary heart disease. Andrew received his MD from the University of Illinois College of Medicine, Chicago in 1986. He received his BA from Cornell University in Ithaca, NY in 1982. Andrew has authored or co-authored more than 65 papers that have appeared in peer-reviewed journals. He is a member of the National Institutes of Health Nuclear Magnetic Resonance Safety Committee and is on the Editorial Board of the Journal of the American College of Cardiology. Andrew is also is a reviewer for several peer-reviewed journals.
Solving the previously impossible is not easy. You need a community to enable and empower your success. As a group we can share experiences, strategies, and information that will truly allow us to affect change at a global scale. The organizations that support the Data Science Bowl form the underpinnings of that community.
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