TY  - JOUR
AU  - Tang, Wentai
AU  - Wang, Jian
AU  - Lin, Hongfei
AU  - Zhao, Di
AU  - Xu, Bo
AU  - Zhang, Yijia
AU  - Yang, Zhihao
PY  - 2022
DA  - 2022/8/2
TI  - A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
JO  - JMIR Med Inform
SP  - e37817
VL  - 10
IS  - 8
KW  - medical relation extraction
KW  - syntactic features
KW  - pruning method
KW  - neural networks
KW  - medical literature
KW  - medical text
KW  - extraction
KW  - syntactic
KW  - classification
KW  - interaction
KW  - text
KW  - literature
KW  - semantic
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2022/8/e37817
UR  - https://doi.org/10.2196/37817
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35917162
DO  - 10.2196/37817
ID  - info:doi/10.2196/37817
ER  -