Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network

Li, Xin; Tang, Xuli; Cheng, Qiuming

doi:10.1016/j.joi.2022.101333

Cited by 14 publications

(5 citation statements)

References 49 publications

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“…However, some studies illustrate benchmark performances in the general biomedical literature. Li and colleagues developed an exhaustive deep learning regression model of nearly 10 million biomedical papers which achieved an r 2 of 0.78 for prediction of citation counts (22). The top performing model in our study achieved an AUC of 0.81.…”

Section: Discussionmentioning

confidence: 99%

What predicts citation counts and translational impact in headache research? A machine learning analysis

Danelakis,

Langseth,

Nachev

et al. 2024

Cephalalgia

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Background We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive. Methods Bibliometric data and the titles, abstracts, and keywords from 8600 publications in three headache-oriented journals from their inception to 31 December 2017 were used. A series of machine learning models were implemented to predict three classes of 5-year citation count intervals (0–5, 6–14 and, >14 citations); and the translational impact of a publication. Models were evaluated out-of-sample with area under the receiver operating characteristics curve (AUC). Results The top performing gradient boosting model predicted correct citation count class with an out-of-sample AUC of 0.81. Bibliometric data such as page count, number of references, first and last author citation counts and h-index were among the most important predictors. Prediction of translational impact worked optimally when including both bibliometric data and information from the title, abstract and keywords, reaching an out-of-sample AUC of 0.71 for the top performing random forest model. Conclusion Citation counts are best predicted by bibliometric data, while models incorporating both bibliometric data and publication content identifies the translational impact of headache research.

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Section: Discussionmentioning

confidence: 99%

What predicts citation counts and translational impact in headache research? A machine learning analysis

Danelakis,

Langseth,

Nachev

et al. 2024

Cephalalgia

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“…Science faces several huge challenges since some of these scenarios can be an opportunity and some a serious threat. Despite this multifaceted challenge, we used AI and could replicate earlier studies (3) using all available publications on headache medicine. The question is: What drives citation counts?…”

Section: What Have I Learned As Editor-in-chief?mentioning

confidence: 99%

Looking at Cephalalgia from the inside

May

2023

Cephalalgia

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“…The effective knowledge flow from basic to clinical is the basis for the success of translational research (Du et al, 2019 ; Li, 2022 ). In bibliometrics, the process of knowledge flow can be quantified by the movement of knowledge from cited papers to citing papers.…”

Section: Related Workmentioning

confidence: 99%

“…The F1 score and accuracy of their experiment were respectively 0.56 and 0.84. Li et al ( 2022 ) designed a multilayer perceptron neural network model with 91 features from three different dimensions (i.e., paper dimension, reference dimension, and citing paper dimension), to predict the clinical citation count of biomedical papers in the future. Features in each dimension can be classified into three categories, including citation-related, clinical translation-related as well as topic-related; the authors concluded that the features in the reference dimension are the most important for the task.…”

Section: Related Workmentioning

confidence: 99%

“…Hutchins et al ( 2019 ) analyzed the paper’s “Translational Potential” which means the probability of whether a paper will be clinically cited, by machine learning models with MeSH-related and citation-related information (Accuracy = 0.56 and F1 score = 0.84). Li et al ( 2022 ) used a multilayer perceptron neural network with 91 features, including citation-related features, clinical translation-related features, and topic-related features to predict the “clinical impact” (i.e., the number of clinical citations) of biomedical papers. (MAE = 0.5132 and R 2 = 0.7883).…”

Section: Introductionmentioning

confidence: 99%

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Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation

Tang

2022

Scientometrics

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Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn’t require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.

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Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network

Cited by 14 publications

References 49 publications

What predicts citation counts and translational impact in headache research? A machine learning analysis

What predicts citation counts and translational impact in headache research? A machine learning analysis

Looking at Cephalalgia from the inside

Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation

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