A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field

Abstract

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers’ abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.

Evaluation of editors’ abilities to predict the citation potential of research manuscripts submitted to The BMJ : a cohort study

“…For each editor, we calculated the number of manuscripts they assigned to the correct citation category. We calculated κ statistics (95% confidence intervals) for each editor, using Fleiss-Cohen weights, 22 as implemented in the R package vcd . 23 We calculated how often each editor’s classification was “extremely wrong” (a highly cited paper was estimated to have low citation potential or vice versa).…”

Ensemble Multifeatured Deep Learning Models and Applications: A Survey

“…Overcoming these challenges is not just a theoretical concern, but also it is vital for the practical applications of computer vision algorithms [44], [45]. The inability to effectively address these issues can result in unreliable performance, thereby limiting the applicability of computer vision in mission-critical scenarios like autonomous vehicles [46], surveillance systems [47], optical character recognition [48], [49], agricultural automation [50], manufacturing and quality inspection [51], augmented reality [52], and medical imaging [53], [54].…”

“…Approaches [129], Ensemble Models [123] Feature-level, Decision-level, Classifierlevel [5] Various NER datasets, Scientific papers [52] Improved performance, Effectiveness in NER [125], [158] Difficulty in handling complex entities, Limited generalization [132] Fine-grained NER, Multi-modal fusion [159] Text Classification Ensemble Algorithms [160], Deep Learning Approaches [161] Feature-level, Decision-level, Classifierlevel [162] Various Text Classification datasets, Cybersecurity [158] Improved classification accuracy, Robustness to noise [163], [164] Difficulty in handling diverse data, Limited interpretability [165] Scalable ensemble techniques, Interpretable ensembles [166] Machine Translation…”

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

“…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 …”

“…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. 9 Additionally, medical student participation in research is associated with improved scientific productivity, including an increase in h-index, and attitudes towards research.…”