Deep learning to refine the identification of high-quality clinical research articles from the biomedical literature: Performance evaluation.
Bioinformatics
Evidence-based medicine
Literature retrieval
Machine learning
Medical informatics
Natural Language Processing
Journal
Journal of biomedical informatics
ISSN: 1532-0480
Titre abrégé: J Biomed Inform
Pays: United States
ID NLM: 100970413
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
15
11
2022
revised:
24
04
2023
accepted:
03
05
2023
medline:
5
6
2023
pubmed:
11
5
2023
entrez:
10
5
2023
Statut:
ppublish
Résumé
Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence. To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice. We fine-tuned variations of BERT models (BERT In training, three of the four selected best performing models were trained using BioBERT Deep learning using pretrained language models and a large dataset of classified articles produced models with improved specificity while maintaining >99% recall. The resulting DL-PLUS model identifies high-quality, clinically relevant articles from PubMed at the time of publication. The model improves the efficiency of a literature surveillance program, which allows for faster dissemination of appraised research.
Sections du résumé
BACKGROUND
Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence.
OBJECTIVE
To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice.
METHODS
We fine-tuned variations of BERT models (BERT
RESULTS
In training, three of the four selected best performing models were trained using BioBERT
CONCLUSIONS
Deep learning using pretrained language models and a large dataset of classified articles produced models with improved specificity while maintaining >99% recall. The resulting DL-PLUS model identifies high-quality, clinically relevant articles from PubMed at the time of publication. The model improves the efficiency of a literature surveillance program, which allows for faster dissemination of appraised research.
Identifiants
pubmed: 37164244
pii: S1532-0464(23)00105-3
doi: 10.1016/j.jbi.2023.104384
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
104384Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.