  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.
