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Cited by 101Biomedical Text Mining and OntologiesInformation Retrieval and Search BehaviorMeta-analysis and systematic reviews

Towards Automatic Recognition of Scientifically Rigorous Clinical Research Evidence

Open access
Kilicoglu, H., Demner-Fushman, D., Rindflesch, T. C., Wilczynski, N. L., Haynes, R. B.·2009·Journal of the American Medical Informatics Association
DOI 10.1197/jamia.m2996PMC PMC2605595
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Abstract

The growing numbers of topically relevant biomedical publications readily available due to advances in document retrieval methods pose a challenge to clinicians practicing evidence-based medicine. It is increasingly time consuming to acquire and critically appraise the available evidence. This problem could be addressed in part if methods were available to automatically recognize rigorous studies immediately applicable in a specific clinical situation. We approach the problem of recognizing studies containing useable clinical advice from retrieved topically relevant articles as a binary classification problem. The gold standard used in the development of PubMed clinical query filters forms the basis of our approach. We identify scientifically rigorous studies using supervised machine learning techniques (Naïve Bayes, support vector machine (SVM), and boosting) trained on high-level semantic features. We combine these methods using an ensemble learning method (stacking). The performance of learning methods is evaluated using precision, recall and F(1) score, in addition to area under the receiver operating characteristic (ROC) curve (AUC). Using a training set of 10,000 manually annotated MEDLINE citations, and a test set of an additional 2,000 citations, we achieve 73.7% precision and 61.5% recall in identifying rigorous, clinically relevant studies, with stacking over five feature-classifier combinations and 82.5% precision and 84.3% recall in recognizing rigorous studies with treatment focus using stacking over word + metadata feature vector. Our results demonstrate that a high quality gold standard and advanced classification methods can help clinicians acquire best evidence from the medical literature.

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  • The optimal type of classifier will depend on variable parameters, such as the proportion of relevant publications in the initial search and the complexity of the inclusion criteria.

    “For example, in systematic reviews, the optimal type of classifier will depend on variable parameters, such as the proportion of relevant publications in the initial search and the complexity of the inclusion criteria used by the researcher 23 .”

    From An open source machine learning framework for efficient and transparent systematic reviews (2021) · cites this paper as [23]

    The Scientific Rigor section says different classifiers serve different information needs, supporting the general point that the best choice depends on the task. The Data Collection section reports the proportion of rigorous articles in its collection, but our copy does not say that the optimal classifier depends on the proportion of relevant results in an initial systematic-review search or on the complexity of a reviewer's inclusion criteria. No page number was supplied for these passages. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Different classification techniques can serve different needs in the retrieval of relevant publications, for example the desired balance between recall and precision.

    “It was found [26, 27] that different classification techniques can serve different needs in the retrieval of relevant publications, for example the desired balance between recall and precision.”

    From Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records (2023) · cites this paper as [26]

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Research works printed but not linked: 4

  1. Hersh W, Buckley C, Leone T, Hickman D. OHSUMED: an interactive retrieval evaluation and new large text collection for research. Proc SIGIR 94. 1994;192-201.
  2. Wilcox A, Hripcsak G, Friedman C. Using Knowledge Sources to Improve Classification of Medical Text Reports. Proc Work- shop on Text Mining (KDD-2000). Boston MA 2000.
  3. Ting KM, Witten IH. Issues in stacked generalization. J Artif Intell Res. 1999;10:271-89.
  4. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T. YALE: Rapid prototyping for complex data mining tasks. Proc SIGKDD 2006;935-40.

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Authors
Kilicoglu, H., Demner-Fushman, D., Rindflesch, T. C., Wilczynski, N. L., & Haynes, R. B.
Affiliations
  • National Institutes of Health (US)
  • McMaster University (CA)
ORCID
Haynes, R. B.
Venue
Journal of the American Medical Informatics Association
Publisher
Oxford University Press (OUP)
Published
2009-01-01
Volume
16
Issue
1
Pages
25-31
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
DOI
10.1197/jamia.m2996
PMC
PMC2605595
OpenAlex
W1779982606
Cited by
101
References
24
Citations by year
2026: 2 · 2025: 3 · 2024: 5 · 2023: 7 · 2022: 1 · 2021: 6 · 2020: 8 · 2019: 5 · 2018: 3 · 2017: 7 · 2016: 4 · 2015: 14 · 2014: 4 · 2013: 3 · 2012: 11
FWCI
2.47
Type
journal article
Language
en
Open access
Locations
  • Journal of the American Medical Informatics Association (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institutes of Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
evidence-based medicine, MEDLINE citations, semantic features, stacking ensemble, naive Bayes, support vector machine, boosting, gold standard annotation
MeSH terms
Artificial Intelligence, Bayes Theorem, ROC Curve, MEDLINE, Information Storage and Retrieval, Biomedical Research, Evidence-Based Practice

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