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An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels
Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble lear...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609520/ https://www.ncbi.nlm.nih.gov/pubmed/26504897 http://dx.doi.org/10.1155/2015/507925 |
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author | Zhang, Gang Huang, Yonghui Zhong, Ling Ou, Shanxing Zhang, Yi Li, Ziping |
author_facet | Zhang, Gang Huang, Yonghui Zhong, Ling Ou, Shanxing Zhang, Yi Li, Ziping |
author_sort | Zhang, Gang |
collection | PubMed |
description | Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low. |
format | Online Article Text |
id | pubmed-4609520 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-46095202015-10-26 An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels Zhang, Gang Huang, Yonghui Zhong, Ling Ou, Shanxing Zhang, Yi Li, Ziping ScientificWorldJournal Research Article Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low. Hindawi Publishing Corporation 2015 2015-10-01 /pmc/articles/PMC4609520/ /pubmed/26504897 http://dx.doi.org/10.1155/2015/507925 Text en Copyright © 2015 Gang Zhang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Gang Huang, Yonghui Zhong, Ling Ou, Shanxing Zhang, Yi Li, Ziping An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels |
title | An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels |
title_full | An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels |
title_fullStr | An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels |
title_full_unstemmed | An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels |
title_short | An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels |
title_sort | ensemble learning based framework for traditional chinese medicine data analysis with icd-10 labels |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4609520/ https://www.ncbi.nlm.nih.gov/pubmed/26504897 http://dx.doi.org/10.1155/2015/507925 |
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