Predicting translational progress in biomedical research

• Published in: PLoS biology Vol. 17; no. 10; p. e3000416
• Main Authors: Hutchins, B. Ian, Davis, Matthew T., Meseroll, Rebecca A., Santangelo, George M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.10.2019; Public Library of Science (PLoS)
• Subjects: Artificial intelligence; Bibliometrics; Biomedical research; Biomedical Research - trends; Citation analysis; Classification; Clinical Trials as Topic; Computer and Information Sciences; Health aspects; Humans; Knowledge; Learning algorithms; Machine Learning; Medical research; Medical Subject Headings-MeSH; Medical treatment; Medicine; Medicine and Health Sciences; Medicine, Experimental; Meta; Methods; National libraries; Periodicals as Topic; Physical Sciences; Practice Guidelines as Topic; Predictions; Research and Analysis Methods; Time Factors; Translation; Translational Research, Biomedical - trends; United States > US; Maryland
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3000416

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.