In a new paper published on October 10, 2019 titled, “Predicting Translational Progress in Biomedical Research,” authors B. Ian Hutchins, Matthew T. Davis, Rebecca A. Meseroll, and George M. Santangelo describe a new way to use artificial intelligence to measure and predict which basic research type findings are likely to be translated into clinical advances. The abstract states:
Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper’s eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community’s early reaction to a paper.
The full paper is available, here. This appears to have the promise of mitigating some significant investment risk.
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