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

arXiv: https://arxiv.org/abs/2210.06346 · HTML: https://mlsanity.com/paper/2210.06346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [Predicting citation counts based on deep neural network learning techniques](https://mlsanity.com/paper/1809.04365/) (2018)
- [The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error](https://mlsanity.com/paper/2009.01812/) (2020)
- [Team size matters: Collaboration and scientific impact since 1900](https://mlsanity.com/paper/1410.8544/) (2014)
- [Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --](https://mlsanity.com/paper/1705.03272/) (2017)
- [Bibliometric author evaluation through linear regression on the coauthor network](https://mlsanity.com/paper/1504.03115/) (2015)
- [Identifying translational science through embeddings of controlled vocabularies](https://mlsanity.com/paper/1812.10609/) (2018)
- [The citation disadvantage of clinical research](https://mlsanity.com/paper/1912.01527/) (2019)
- [Finding citations for PubMed: A large-scale comparison between five freely available bibliographic data sources](https://mlsanity.com/paper/2111.00172/) (2021)

## Built on by (most cited first, 1 of 1)

- [MIRAI: Prediction and Generation of High-Impact Academic Research](https://mlsanity.com/paper/2606.05443/) (2026)

## Abstract (arXiv)

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.