Researchers said the combination of machine learning and experimental design could have a broad impact on the computational community and would be useful for any large study interested in ensuring the best use of resources. And for clinicians, it could provide new insights into certain risk factors to monitor, as well as inform future clinical studies.

The team wants to apply the new framework to other diseases like coronary artery disease and follow up on the CoA work to better understand why certain physiological factors are more crucial to determining health risk. While the ultimate goal is to see the models used in a clinical environment, a more comprehensive study on the impacts of certain factors on CoA will need to be done, researchers said. Further work will require partnerships with clinicians and more datasets from patients with known outcomes, according to Draeger.


For now, predictions based on medical imaging and simulation still require a great deal of time and effort to generate an actionable result, Draeger said. But as researchers perform more studies, it is likely that such neural networks and models can be refined so that fewer simulations will be needed to make predictions that clinicians can have confidence in.

Draeger said by leveraging its expertise in physics, simulation, applied math and machine learning, as well as its access to supercomputers, LLNL is in a strong position to partner with biologists to impact medicine and health in the future through high performance computing modeling and simulation.

“We’re just now getting to the point that high performance computing and simulation is at enough fidelity and speed that you can actually cross over directly with clinical medicine. Draeger said. “We’ve been getting closer and closer but invariably, simulations are too slow. But we’re now at a point where it’s not impractical, especially with machine learning to cut down on the costs, to imagine that you could actually do a simulation study of a specific person and use it to impact their care in the not-too-distant future.”

Funding for the work at LLNL was provided by the Laboratory Directed Research and Development (LDRD) program and the Lab’s Institutional Computing Grand Challenge program. Further grant money for the study was made available by the National Institutes of Health.

Co-authors included researchers from Duke University, Brigham and Women’s Hospital at Harvard Medical School, Arizona State University, the Dana-Farber Cancer Institute, the Harvard T.H. Chan School of Public Health, Harvard University and the Broad Institute of Harvard and MIT.

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