论文标题
使用多层感知神经网络预测生物医学论文的临床引用计数
Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network
论文作者
论文摘要
从临床指南或临床试验中收到的临床引用数量已被认为是量化生物医学论文临床影响的最合适指标之一。因此，生物医学论文的临床引用计数的早期预测对于生物医学中的科学活动至关重要，例如研究评估，资源分配和临床翻译。在这项研究中，我们设计了一个四层多层感知神经网络（MPNN）模型，以预测未来生物医学论文的临床引用计数，它使用1985年至2005年发表的9,822,620份生物医学论文进行了9,822,620篇生物医学论文。尺寸，并在引用纸张尺寸中进行三十五。在每个维度中，这些功能可以分为三类，即引用相关的功能，与临床翻译相关的功能以及与主题相关的功能。此外，在论文维度中，我们还考虑了以前已证明与研究论文的引用计数有关的特征。结果表明，所提出的MPNN模型的表现优于其他五个基线模型，并且参考维度中的特征是最重要的。
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.
