Automatic recognition of self-acknowledged limitations in clinical research literature

Halil Kilicoglu, Graciela Rosemblat, Mario Malički, Gerben Ter Riet

Research output: Contribution to journalArticlepeer-review


Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Methods: To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: A rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results: Annotators had good agreement in labeling limitation sentences (Krippendorff's α= 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]). Conclusions: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

Original languageEnglish (US)
Pages (from-to)855-861
Number of pages7
JournalJournal of the American Medical Informatics Association
Issue number7
StatePublished - Jul 1 2018
Externally publishedYes


  • clinical research literature
  • natural language processing
  • research transparency
  • self-acknowledged limitations

ASJC Scopus subject areas

  • Health Informatics


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