@Article{cmc.2020.010914,
AUTHOR = {Baili Zhang, Shan Zhou, Le Yang, Jianhua Lv, Mingjun Zhong},
TITLE = {Study on Multi-Label Classification of Medical Dispute Documents},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {1975--1986},
URL = {http://www.techscience.com/cmc/v65n3/40150},
ISSN = {1546-2226},
ABSTRACT = {The Internet of Medical Things (IoMT) will come to be of great importance in 
the mediation of medical disputes, as it is emerging as the core of intelligent medical 
treatment. First, IoMT can track the entire medical treatment process in order to provide 
detailed trace data in medical dispute resolution. Second, IoMT can infiltrate the ongoing 
treatment and provide timely intelligent decision support to medical staff. This 
information includes recommendation of similar historical cases, guidance for medical 
treatment, alerting of hired dispute profiteers etc. The multi-label classification of 
medical dispute documents (MDDs) plays an important role as a front-end process for 
intelligent decision support, especially in the recommendation of similar historical cases. 
However, MDDs usually appear as long texts containing a large amount of redundant 
information, and there is a serious distribution imbalance in the dataset, which directly 
leads to weaker classification performance. Accordingly, in this paper, a multi-label 
classification method based on key sentence extraction is proposed for MDDs. The 
method is divided into two parts. First, the attention-based hierarchical bi-directional long 
short-term memory (BiLSTM) model is used to extract key sentences from documents; 
second, random comprehensive sampling Bagging (RCS-Bagging), which is an ensemble 
multi-label classification model, is employed to classify MDDs based on key sentence 
sets. The use of this approach greatly improves the classification performance. 
Experiments show that the performance of the two models proposed in this paper is 
remarkably better than that of the baseline methods.},
DOI = {10.32604/cmc.2020.010914}
}