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AI and big data predict which research will influence future medical treatments

October 10, 2019 / NA

An artificial intelligence/machine learning model to predict which scientific advances are likely to eventually translate to the clinic has been developed by Ian Hutchins and colleagues in the Office of Portfolio Analysis (OPA), a team led by George Santangelo at the National Institutes of Health (NIH). This work, described in a Meta-Research article published October 10 in the open-access journal PLOS Biology, aims to decrease the sometimes decades-long interval between scientific discovery and clinical application; the method determines the likelihood that a research article will be cited by a future clinical trial or guideline, an early indicator of translational progress. Hutchins and colleagues have quantified these predictions, which are highly accurate with as little as two years of post-publication data, as a novel metric called "Approximate Potential to Translate" (APT). APT values can be used by researchers and decision-makers to focus attention on areas of science ...