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 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.


CONCLUSIONS
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.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Gang
G
Zhang
School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Yonghui
Y
Huang
School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Ling
L
Zhong
Department of Radiology, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, China.
Shanxing
S
Ou
Department of Plastic and Reconstructive Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
Yi
Y
Zhang
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, China.
Ziping
Z
Li
D015669:Acupuncture Points; D000465:Algorithms; D019992:Databases as Topic; D006801:Humans; D038801:International Classification of Diseases; D008516:Medicine, Chinese Traditional; D013223:Statistics as Topic