Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network |
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| Institution: | 1. School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China;2. School of Information Management, Central China Normal University, Wuhan 430079, Hubei, China;3. School of Information Management, Wuhan University, Wuhan 430074, Hubei, China;1. School of Information Management, Wuhan University, Wuhan 430072, China;2. Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan 430072, China;3. Department of Information Management, Peking University, Beijing 100871, China;1. School of Information Management, Nanjing University, Nanjing 210032, China;2. Department of Information Management, Peking University, Beijing 100871, China;1. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, Shanxi 030006, China;2. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China;3. Engineering Research Center for Machine Vision and Data Mining of Shanxi Province, Taiyuan, Shanxi 030006, China;4. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, China;5. School of Information, Shanxi University of Finance and Economics, Taiyuan, Shanxi 030006, China;1. School of Information Management, Nanjing University, Nanjing, China;2. Centre for R&D Monitoring (ECOOM) and Department MSI, KU Leuven, Belgium;3. Faculty of Social Sciences, University of Antwerp, Belgium;4. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China |
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| 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 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. In all the three dimensions, the citation-related and topic-related features were more important than the clinical translation-related features for the prediction. It also turned out that the features helpful in predicting the citation count of papers are not important for predicting the clinical citation count of biomedical papers. Furthermore, we explored the MPNN model based on different categories of biomedical papers. The results showed that the clinical translation-related features were more important for the prediction of clinical citation count of basic papers rather than those papers closer to clinical science. This study provided a novel dimension (i.e., the reference dimension) for the research community and could be applied to other related research tasks, such as the research assessment for translational programs. In addition, the findings in this study could be useful for biomedical authors (especially for those in basic science) to get more attention from clinical research. |
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