Automatic identification of high impact articles in PubMed to support clinical decision making

Objectives: The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians' retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed (R). Methods: Our machine learning algorithms use a variety of features including bibliometric features (e.g., citation count), social media attention, journal impact factors, and citation metadata. The algorithms were developed and evaluated with a gold standard composed of 502 high impact clinical studies that are referenced in 11 clinical evidence-based guidelines on the treatment of various diseases. We tested the following hypotheses: (1) our high impact classifier outperforms a state-of-the-art classifier based on citation metadata and citation terms, and PubMed's (R) relevance sort algorithm; and (2) the performance of our high impact classifier does not decrease significantly after removing proprietary features such as citation count. Results: The mean top 20 precision of our high impact classifier was 34% versus 11% for the state-of-the-art classifier and 4% for PubMed's (R) relevance sort (p = 0.009); and the performance of our high impact classifier did not decrease significantly after removing proprietary features (mean top 20 precision = 34% vs. 36%; p = 0.085). Conclusion: The high impact classifier, using features such as bibliometrics, social media attention and MEDLINE (R) metadata, outperformed previous approaches and is a promising alternative to identifying high impact studies for clinical decision support. (C) 2017 Elsevier Inc. All rights reserved.