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Study on Multi-Label Classification of Medical Dispute Documents

Computers, Materials & Continua 2020年第12期65卷 1975-1986页

作者： Baili Zhang Shan Zhou Le Yang Jianhua Lv Mingjun Zhong School of Computer Science and Engineering Southeast UniversityNanjing21189China key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast UniversityNanjing211189China Research Center for Judicial Big Data Supreme Count of ChinaNanjing211189China University of Lincoln LincolnLN67TSUK

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 ***,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute ***,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical *** information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers *** 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 ***,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 ***,in this paper,a multi-label classification method based on key sentence extraction is proposed for *** method is divided into two ***,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 *** use of this approach greatly improves the classification *** show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.

关键词： Internet of Medical Things(IoMT) medical disputes medical dispute document(MDD) multi-label classification(MLC) key sentence extraction class imbalance

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