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.

本研究利用1985年至2005年的9822620篇医学论文，使用一个四层的多层感知神经网络模型，通过提取论文维度、参考文献维度和引用论文维度的91个特征，包括与引用、临床翻译和主题相关的特征，以及先前已证明与引用计数相关的特征，来预测未来医学论文的临床引用计数。结果表明，所提出的多层感知神经网络模型优于其他五种基线模型，参考文献维度中的特征最为重要。

使用多层感知器神经网络预测生物医学论文的临床引用次数