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

Highlights：

• The most important features for predicting clinical count of biomedical papers are features in the reference dimension, which have been ignored in previous research. Meanwhile, clinical translation-related features are important for predicting clinical count of basic papers but not the papers closer to clinical research.

• Features that have previously demonstrated to be highly related to the citation count of academic papers, are not important for the clinical citation count prediction of biomedical papers.

• This study could be useful for policymakers and pharmaceutical companies to early assess the translational progress of biomedical research and to monitor the biomedical research with high potential to be clinically translated in real time.

摘要

•This study predicted the clinical citation count of biomedical papers using a multilayer perceptron neural network model, which outperformed other five baseline models (i.e., linear regression, support vector machine, random forest, KNN, and XGBoost).•The most important features for predicting clinical count of biomedical papers are features in the reference dimension, which have been ignored in previous research. Meanwhile, clinical translation-related features are important for predicting clinical count of basic papers but not the papers closer to clinical research.•Features that have previously demonstrated to be highly related to the citation count of academic papers, are not important for the clinical citation count prediction of biomedical papers.•This study could be useful for policymakers and pharmaceutical companies to early assess the translational progress of biomedical research and to monitor the biomedical research with high potential to be clinically translated in real time.