Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE Article Swipe
Ahmed A. Al‐Jaishi
,
Monica Taljaard
,
Melissa D Al-Jaishi
,
Sheikh S. Abdullah
,
Lehana Thabane
,
P.J. Devereaux
,
Stephanie N. Dixon
,
Amit X. Garg
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-970738/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-970738/v1
Background: Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieval from bibliographic databases difficult. Machine learning algorithms may improve their identification and retrieval. Therefore, we aimed to develop machine learning algorithms that accurately determine whether a bibliographic citation is a CRT report. Methods: We trained, internally validated, and externally validated two convolutional neural networks and one support vector machines (SVM) algorithms to predict whether a citation is a CRT report or not. We exclusively used the information in an article citation, including the title, abstract, keywords, and subject headings. The algorithms' output was a probability from 0 to 1. We assessed algorithm performance using the area under the receiver operating characteristic (AUC) curves. Each algorithm's performance was evaluated individually and together as an ensemble. We randomly selected 5000 from 87,633 citations to train and internally validate our algorithms. Of the 5000 selected citations, 589 (12%) were confirmed CRT reports. We then externally validated our algorithms on an independent set of 1916 randomized trial citations, with 665 (35%) confirmed CRT reports. Results: In internal validation, the ensemble algorithm discriminated best for identifying CRT reports with an AUC of 98.6% (95% confidence interval: 97.8%, 99.4%), sensitivity of 97.7% (94.3%, 100%), and specificity of 85.0% (81.8%, 88.1%). In external validation, the ensemble algorithm had an AUC of 97.8 % (97.0%, 98.5%), sensitivity of 97.6% (96.4%, 98.6%), and specificity of 78.2% (75.9%, 80.4%)). All three individual algorithms performed well, but less so than the ensemble. Conclusions: We successfully developed high-performance algorithms that identified whether a citation was a CRT report with high sensitivity and moderately high specificity. We provide open-source software to facilitate the use of our algorithms in practice.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-970738/v1
- https://www.researchsquare.com/article/rs-970738/latest.pdf
- OA Status
- green
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3206407893
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3206407893Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-970738/v1Digital Object Identifier
- Title
-
Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASEWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-21Full publication date if available
- Authors
-
Ahmed A. Al‐Jaishi, Monica Taljaard, Melissa D Al-Jaishi, Sheikh S. Abdullah, Lehana Thabane, P.J. Devereaux, Stephanie N. Dixon, Amit X. GargList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-970738/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-970738/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-970738/latest.pdfDirect OA link when available
- Concepts
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MEDLINE, Computer science, Randomized controlled trial, Cluster (spacecraft), Machine learning, Algorithm, Artificial intelligence, Medicine, Internal medicine, Biology, Programming language, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2021-10-21 |
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