Automatic Identification of Recent High Impact Clinical Articles in PubMed to Support Clinical Decision Making Using Time-agnostic Features

Jiantao Bian, Samir Abdelrahman, Jianlin Shi, Guilherme Del Fiol
<span title="2018-11-20">2018</span> <i title="Elsevier"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p4kk6lusgrhyxecgig72iasi5q" style="color: black;">Journal of Biomedical Informatics</a> </i> &nbsp;
Finding recent clinical studies that warrant changes in clinical practice ("high impact" clinical studies) in a timely manner is very challenging. We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance. To identify recent studies, we developed our classification model using time-agnostic features that are available as soon as an article is indexed in PubMed®, such as journal impact factor,
more &raquo; ... r count, and study sample size. Using a gold standard of 541 high impact treatment studies referenced in 11 disease management guidelines, we tested the following null hypotheses: 1) the high impact classifier with time-agnostic features (HI-TA) performs equivalently to PubMed's Best Match sort and a MeSH-based Naïve Bayes classifier; and 2) HI-TA performs equivalently to the high impact classifier with both time-agnostic and time-sensitive features (HI-TS) enabled in a previous study. The primary outcome for both hypotheses was mean top 20 precision. The differences in mean top 20 precision between HI-TA and three baselines (PubMed's Best Match, a MeSH-based Naïve Bayes classifier, and HI-TS) were not statistically significant (12% vs. 3%, p=0.101; 12% vs. 11%, p=0.720; 12% vs. 25%, p=0.094, respectively). Recall of HI-TA was low (7%). HI-TA had equivalent performance to state-of-the-art approaches that depend on time-sensitive features. With the advantage of relying only on time-agnostic features, the proposed approach can be used as an adjunct to help clinicians identify recent high impact clinical studies to support clinical decision-making. However, low recall limits the use of HI-TA for literature surveillance.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jbi.2018.11.010">doi:10.1016/j.jbi.2018.11.010</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30468912">pmid:30468912</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6342626/">pmcid:PMC6342626</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/256u3rzvojax3m5klgmcp3dkg4">fatcat:256u3rzvojax3m5klgmcp3dkg4</a> </span>
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