Automatic recognition of self-acknowledged limitations in clinical research literature.
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...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 25; no. 7; pp. 855 - 862 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130459181&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130459181 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jul2018 vid: 25 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 130459181 130459181 NLM29718377 130459181 10.1093/jamia/ocy038 NLM29718377 130459181 ppf: 855 ppct: 7 formats: tig: atl: Automatic recognition of self-acknowledged limitations in clinical research literature. aug: au: Kilicoglu, Halil Rosemblat, Graciela Malički, Mario Riet, Gerben ter Malicki, Mario Ter Riet, Gerben affil: Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD, USA sug: subj: Communications Media Standards Research, Medical Standards Natural Language Processing PubMed Logistic Regression Human ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|