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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/507925 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560320456949760 |
|---|---|
| author | Gang Zhang Yonghui Huang Ling Zhong Shanxing Ou Yi Zhang Ziping Li |
| author_facet | Gang Zhang Yonghui Huang Ling Zhong Shanxing Ou Yi Zhang Ziping Li |
| author_sort | Gang Zhang |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-78313f201fb641938b474e9adfea5e14 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-78313f201fb641938b474e9adfea5e142025-02-03T01:27:51ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/507925507925An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 LabelsGang Zhang0Yonghui Huang1Ling Zhong2Shanxing Ou3Yi Zhang4Ziping Li5School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Radiology, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, ChinaDepartment of Plastic and Reconstructive Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, ChinaThe Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, ChinaObjective. 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.http://dx.doi.org/10.1155/2015/507925 |
| spellingShingle | Gang Zhang Yonghui Huang Ling Zhong Shanxing Ou Yi Zhang Ziping Li An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels The Scientific World Journal |
| 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 |
| url | http://dx.doi.org/10.1155/2015/507925 |
| work_keys_str_mv | AT gangzhang anensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT yonghuihuang anensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT lingzhong anensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT shanxingou anensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT yizhang anensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT zipingli anensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT gangzhang ensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT yonghuihuang ensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT lingzhong ensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT shanxingou ensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT yizhang ensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels AT zipingli ensemblelearningbasedframeworkfortraditionalchinesemedicinedataanalysiswithicd10labels |