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A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study

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 e...

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Autores principales: Tang, Wentai, Wang, Jian, Lin, Hongfei, Zhao, Di, Xu, Bo, Zhang, Yijia, Yang, Zhihao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382554/
https://www.ncbi.nlm.nih.gov/pubmed/35917162
http://dx.doi.org/10.2196/37817
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author Tang, Wentai
Wang, Jian
Lin, Hongfei
Zhao, Di
Xu, Bo
Zhang, Yijia
Yang, Zhihao
author_facet Tang, Wentai
Wang, Jian
Lin, Hongfei
Zhao, Di
Xu, Bo
Zhang, Yijia
Yang, Zhihao
author_sort Tang, Wentai
collection PubMed
description 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.
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spelling pubmed-93825542022-08-18 A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study Tang, Wentai Wang, Jian Lin, Hongfei Zhao, Di Xu, Bo Zhang, Yijia Yang, Zhihao JMIR Med Inform Original Paper 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. JMIR Publications 2022-08-02 /pmc/articles/PMC9382554/ /pubmed/35917162 http://dx.doi.org/10.2196/37817 Text en ©Wentai Tang, Jian Wang, Hongfei Lin, Di Zhao, Bo Xu, Yijia Zhang, Zhihao Yang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.08.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Tang, Wentai
Wang, Jian
Lin, Hongfei
Zhao, Di
Xu, Bo
Zhang, Yijia
Yang, Zhihao
A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
title A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
title_full A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
title_fullStr A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
title_full_unstemmed A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
title_short A Syntactic Information–Based Classification Model for Medical Literature: Algorithm Development and Validation Study
title_sort syntactic information–based classification model for medical literature: algorithm development and validation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382554/
https://www.ncbi.nlm.nih.gov/pubmed/35917162
http://dx.doi.org/10.2196/37817
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