Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research

Objective Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) assoc...

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Veröffentlicht in:BMC medical research methodology Jg. 24; H. 1; S. 108 - 7
Hauptverfasser: Du, Jingcheng, Soysal, Ekin, Wang, Dong, He, Long, Lin, Bin, Wang, Jingqi, Manion, Frank J., Li, Yeran, Wu, Elise, Yao, Lixia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 09.05.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Abstracting and Indexing - methods
Algorithms
Application programming interface
Article screening
Automation
Bibliographic data bases
Classification
Cost control
Deep Learning
Economics, Medical
Evaluation
Health economics
Health Sciences
Human papillomavirus
Humans
Keywords
Literature reviews
Machine Learning
Medical economics
Medical research
Medical screening
Medicine
Medicine & Public Health
Medicine, Experimental
Metadata
Methods
Natural language
Outcome Assessment, Health Care - methods
Papillomavirus Infections - diagnosis
Pediatrics
R&D
Regression analysis
Research & development
Statistical Theory and Methods
Statistics for Life Sciences
Subject specialists
Support vector machines
Systematic literature review
Text categorization
Text classification
Theory of Medicine/Bioethics
Treatment outcome
United States
Deep learning
Article screening
Machine learning
Text classification
Systematic literature review
ISSN:1471-2288, 1471-2288
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Zusammenfassung:Objective Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening. Methods This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms. Results and conclusions The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ObjectType-Literature Review-2
ObjectType-Feature-3
ObjectType-Feature-2
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ObjectType-Undefined-3
ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-024-02224-3