JMIR Publications

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 two methods for enhancing syntactic information in the model. First, we introduced shallow syntactic information into the convolutional neural network to enhance non-local syntactic interactions. Secondly, we devise a cross-domain pruning method to equalize local and non-local syntactic interactions.

Results:

We experimented with three datasets related to the classification of medical literature. The F1 values were 65.5% and 91.5% on the CPR and PGR datasets, and the accuracy was 88.7% on the PubMed dataset. Our model outperforms the current state-of-the-art baseline model in the experiments.

Conclusions:

Our model based on syntactic information effectively enhances the medical relation extraction. Furthermore, the results of the experiments show that shallow syntactic information helps obtain non-local interaction in sentences and effectively reinforces syntactic features. It also provides new ideas for future research directions.