@Article{info:doi/10.2196/37817，作者=“唐文泰与王，健与林，洪飞与赵，迪与徐，博与张，易佳与杨志浩”，标题=“基于句法信息的医学文献分类模型:算法开发与验证研究”，期刊=“JMIR Med Inform”，年=“2022”，月=“8”，日=“2”，卷=“10”，号=“8”，页=“e37817”，关键词=“医学关系提取”;句法功能;修剪方法;神经网络;医学文献;医疗文本;提取;语法;分类;相互作用; text; literature; semantic", abstract="Background: The ever-increasing volume of medical literature necessitates the classification of medical literature. Medical relation extraction is a typical method of classifying a large volume of medical literature. With the development of arithmetic power, medical relation extraction models have evolved from rule-based models to neural network models. The single neural network model discards the shallow syntactic information while discarding the traditional rules. Therefore, we propose a syntactic information--based classification model that complements and equalizes syntactic information to enhance the model. Objective: We aim to complete a syntactic information--based relation extraction model for more efficient medical literature classification. Methods: We devised 2 methods for enhancing syntactic information in the model. First, we introduced shallow syntactic information into the convolutional neural network to enhance nonlocal syntactic interactions. Second, we devise a cross-domain pruning method to equalize local and nonlocal syntactic interactions. Results: We experimented with 3 data sets related to the classification of medical literature. The F1 values were 65.5{\%} and 91.5{\%} on the BioCreative ViCPR (CPR) and Phenotype-Gene Relationship data sets, respectively, and the accuracy was 88.7{\%} on the PubMed data set. Our model outperforms the current state-of-the-art baseline model in the experiments. Conclusions: Our model based on syntactic information effectively enhances medical relation extraction. Furthermore, the results of the experiments show that shallow syntactic information helps obtain nonlocal interaction in sentences and effectively reinforces syntactic features. It also provides new ideas for future research directions. ", issn="2291-9694", doi="10.2196/37817", url="https://medinform.www.mybigtv.com/2022/8/e37817", url="https://doi.org/10.2196/37817", url="http://www.ncbi.nlm.nih.gov/pubmed/35917162" }