Citation count for ophthalmology articles can be successfully predicted with machine learning

Bacchi, Stephen; Teoh, Sheng Chieh; Lam, Lydia; Lam, Antoinette; Casson, Robert J.; Chan, Weng Onn

doi:10.1111/ceo.14087

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…This train-test split was performed once. These preprocessing methods and the models used below were selected, as similar methods had previously proved effective in analyzing scientific abstracts [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

et al. 2023

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Background Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. Methods PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms “ophthalmology,” “radiology,” and “neurology.” Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. Results There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression. Conclusions Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.

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Section: Methodsmentioning

confidence: 99%

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

et al. 2023

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“…It would be useful to have a similar, modified formula present for those starting their medical career, to analyse the trajectory of their h‐ index and future research output. Machine learning algorithms have been shown to predict citation counts in ophthalmology 7 and otology 8 …”

Section: Measures Of Success In Research How To Achieve Itmentioning

confidence: 99%

“…6 It would be useful to have a similar, modified formula present for those starting their medical career, to analyse the trajectory of their h-index and future research output. Machine learning algorithms have been shown to predict citation counts in ophthalmology 7 and otology. 8 Publications as a medical student are associated with increased publication in the mainstream medical literature after graduation, completion of higher degrees, and higher attainment of future faculty positions.…”

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Should research success be predicted amongst medical students and junior doctors?

Gowda

Bacchi

Kovoor

et al. 2023

ANZ Journal of Surgery

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“…There are many other applications of AI in our field beyond patient care. Just one example is presented in the letter by Bacchi et al, 10 which describes the use of AI to predict which articles in the field will be most impactful, based on number of citations. Using almost 100 000 articles published between 2000 and 2021 and identified in the PubMed database, the team developed an algorithm that used year, title, authors, abstract, medical subject headings or MeSH, and journal to predict which articles would be most highly or most lowly cited.…”

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confidence: 99%

Welcoming artificial intelligence into ophthalmology

Smith

2023

Clinical Exper Ophthalmology

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Citation count for ophthalmology articles can be successfully predicted with machine learning

Cited by 3 publications

References 2 publications

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Should research success be predicted amongst medical students and junior doctors?

Welcoming artificial intelligence into ophthalmology

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