Automatic recognition of self-acknowledged limitations in clinical research literature

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

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)855-861
JournalJournal of the American Medical Informatics Association : JAMIA
Volume25
Issue number7
DOIs
Publication statusPublished - Jul 2018

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