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

TL;DR

This study develops a multilayer perceptron neural network to predict future clinical citation counts of biomedical papers, aiding research evaluation and clinical translation efforts.

Contribution

The paper introduces a novel MPNN model utilizing extensive multi-dimensional features to accurately forecast clinical citations of biomedical literature.

Findings

MPNN outperforms five baseline models in prediction accuracy.

Reference-related features are the most influential for citation prediction.

Model demonstrates potential for early impact assessment of biomedical papers.

Abstract

The number of clinical citations received from clinical guidelines or clinical trials has been considered as one of the most appropriate indicators for quantifying the clinical impact of biomedical papers. Therefore, the early prediction of the clinical citation count of biomedical papers is critical to scientific activities in biomedicine, such as research evaluation, resource allocation, and clinical translation. In this study, we designed a four-layer multilayer perceptron neural network (MPNN) model to predict the clinical citation count of biomedical papers in the future by using 9,822,620 biomedical papers published from 1985 to 2005. We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension. In each dimension, the features can be classified into three categories, i.e., the citation-related features, the clinical translation-related features, and the topic-related features. Besides, in the paper dimension, we also considered the features that have previously been demonstrated to be related to the citation counts of research papers. The results showed that the proposed MPNN model outperformed the other five baseline models, and the features in the reference dimension were the most important.