Get access

Automating the process of critical appraisal and assessing the strength of evidence with information extraction technology

Authors

  • Jou-Wei Lin MD MPH PhD,

    1. Attending Cardiologist, Cardiovascular Center, National Taiwan University Hospital Yun-Lin Branch, Dou-Liou City, Taiwan and Associate Professor, Department of Medicine, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan
    Search for more papers by this author
    • These two authors contributed equally to this work.

  • Chia-Hsuin Chang MD ScD,

    1. Attending Physician and Assistant Professor, Department of Medicine, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan
    Search for more papers by this author
    • These two authors contributed equally to this work.

  • Ming-Wei Lin MS,

    1. Graduate Student
    Search for more papers by this author
  • Mark H. Ebell MD MS,

    1. Associate Professor and Co-Director, Institute for Evidence-Based Health Professions Education, Athens, GA, USA
    Search for more papers by this author
  • Jung-Hsien Chiang PhD

    Corresponding author
    1. Professor, Institute of Medical Informatics and Department of Computer Science, National Cheng Kung University, Tainan, Taiwan
      Dr Jung-Hsien Chiang, Department of Computer Science, National Cheng Kung University, Tainan, Taiwan. E-mail: jchiang@mail.ncku.edu.tw
    Search for more papers by this author

Dr Jung-Hsien Chiang, Department of Computer Science, National Cheng Kung University, Tainan, Taiwan. E-mail: jchiang@mail.ncku.edu.tw

Abstract

Background  Critical appraisal, one of the most crucial steps in the practice of evidence-based medicine, is expertise-dependent and time-consuming. The objective of this study was to develop and evaluate an automated text-mining system that could determine the evidence level provided by a medical article.

Methods  A text processor was designed and built to interpret the abstracts of medical literature. The system extracted information about: (1) the impact factor of the journal; (2) study design; (3) human subject involvement; (4) number of subjects; (5) P-value; and (6) confidence intervals. We used a classification tree algorithm (C4.5) to create a decision tree using supervised classification. Each article was categorized into evidence level A, B or C, and the output was compared to that determined by domain experts (the reference standard).

Results  We used a corpus of 3180 cardiovascular disease original research articles, of which 1108 were previously assigned evidence level A, 1705 level B and 367 level C by domain experts. The abstracts were analysed by our automated system and an evidence level was assigned. The algorithm accurately classified 85% of the articles. The agreement between computer and domain experts was substantial (κ-value: 0.78). Cross-validation showed consistent results across repeated tests.

Conclusion  The automated engine accurately classified the evidence level. Misclassification might have resulted from incomplete information retrieval and inaccurate data extraction. Further efforts will focus on assessing relevance and using additional study design features to refine evidence level classification.

Get access to the full text of this article

Ancillary