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A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining
Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of gr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579813/ https://www.ncbi.nlm.nih.gov/pubmed/37854059 http://dx.doi.org/10.3389/fgene.2023.1272016 |
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author | Gong, Lejun Jiang, Jindou Chen, Shiqi Qi, Mingming |
author_facet | Gong, Lejun Jiang, Jindou Chen, Shiqi Qi, Mingming |
author_sort | Gong, Lejun |
collection | PubMed |
description | Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of biomdical text mining technology, TCM has entered the era of intelligence that based on data, and model training increasingly relies on the large-scale labeled data. However, it is difficult to form a large standard data set in the field of TCM due to the low degree of standardization of TCM data collection and the privacy protection of patients’ medical records. To solve the above problem, a multi-label deep forest model based on an improved multi-label ReliefF feature selection algorithm, ML-PRDF, is proposed to enhance the representativeness of features within the model, express the original information with fewer features, and achieve optimal classification accuracy, while alleviating the problem of high data processing cost of deep forest models and achieving effective TCM discriminative analysis under small samples. The results show that the proposed model finally outperforms other multi-label classification models in terms of multi-label evaluation criteria, and has higher accuracy in the TCM syndrome differentiation problem compared with the traditional multi-label deep forest, and the comparative study shows that the use of PCC-MLRF algorithm for feature selection can better select representative features. |
format | Online Article Text |
id | pubmed-10579813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105798132023-10-18 A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining Gong, Lejun Jiang, Jindou Chen, Shiqi Qi, Mingming Front Genet Genetics Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of biomdical text mining technology, TCM has entered the era of intelligence that based on data, and model training increasingly relies on the large-scale labeled data. However, it is difficult to form a large standard data set in the field of TCM due to the low degree of standardization of TCM data collection and the privacy protection of patients’ medical records. To solve the above problem, a multi-label deep forest model based on an improved multi-label ReliefF feature selection algorithm, ML-PRDF, is proposed to enhance the representativeness of features within the model, express the original information with fewer features, and achieve optimal classification accuracy, while alleviating the problem of high data processing cost of deep forest models and achieving effective TCM discriminative analysis under small samples. The results show that the proposed model finally outperforms other multi-label classification models in terms of multi-label evaluation criteria, and has higher accuracy in the TCM syndrome differentiation problem compared with the traditional multi-label deep forest, and the comparative study shows that the use of PCC-MLRF algorithm for feature selection can better select representative features. Frontiers Media S.A. 2023-10-03 /pmc/articles/PMC10579813/ /pubmed/37854059 http://dx.doi.org/10.3389/fgene.2023.1272016 Text en Copyright © 2023 Gong, Jiang, Chen and Qi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Gong, Lejun Jiang, Jindou Chen, Shiqi Qi, Mingming A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining |
title | A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining |
title_full | A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining |
title_fullStr | A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining |
title_full_unstemmed | A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining |
title_short | A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining |
title_sort | syndrome differentiation model of tcm based on multi-label deep forest using biomedical text mining |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579813/ https://www.ncbi.nlm.nih.gov/pubmed/37854059 http://dx.doi.org/10.3389/fgene.2023.1272016 |
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