Centre of Expertise Urban Vitality

Automatic recognition of self-acknowledged limitations in clinical research literature


<p>Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.</p><p>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).</p><p>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]).</p><p>Conclusions: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.</p>

Reference Kilicoglu, H., Rosemblat, G., Malički, M., & Ter Riet, G. (2018). Automatic recognition of self-acknowledged limitations in clinical research literature. Journal of the American Medical Informatics Association : JAMIA, 25(7), 855-861. https://doi.org/10.1093/jamia/ocy038
Published by  Urban Vitality 1 July 2018

Publication date

Jul 2018


Halil Kilicoglu
Graciela Rosemblat
Mario Malički

Research database