Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jul 1;25(7):855-861.
doi: 10.1093/jamia/ocy038.

Automatic Recognition of Self-Acknowledged Limitations in Clinical Research Literature

Affiliations
Free PMC article

Automatic Recognition of Self-Acknowledged Limitations in Clinical Research Literature

Halil Kilicoglu et al. J Am Med Inform Assoc. .
Free PMC article

Abstract

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.

Similar articles

See all similar articles

Cited by 1 article

Publication types

Feedback