摘要：
International Classification of Diseases (ICD) code is an important label of electronic health record. The automatic ICD code assignment based on the narrative of clinical documents is an essential task which has drawn much attention recently. When Chinese clinical notes are the input corpus, the nature of Chinese brings some issues that need to be considered, such as the accuracy of word segmentation and the representation of single Chinese characters which contain semantics. Taking the lengthy text of patient notes and the representation of Chinese words into account, we present a multilayer attention bidirectional recurrent neural network (MA-BiRNN) model to implement the assignment of disease codes. A hierarchical approach is used to represent the feature of discharge summaries without manual feature engineering. The combination of character level embedding and word level embedding can improve the representation of words. Attention mechanism is introduced into bidirectional long short term memory networks, which helps to solve the performance dropping problem when plain recurrent neural networks encounter long text sequences. The experiment is carried out on a real-world dataset containing 7732 admission records in Chinese and 1177 unique ICD-10 labels. The proposed model achieves 0.639 and 0.766 in F1-score on full-level code and block-level code, respectively. It outperforms the baseline neural network models and achieves the lowest Hamming loss value. Ablation analysis indicates that the multilevel attention mechanism plays a decisive role in the system for dealing with Chinese clinical notes.
作者机构：
[刘立; 罗扬; 汪琳霞; 刘芳菊; 李悛] School of Computer Science and Technology, University of South China, Hengyang;Hunan;421001, China;[刘立; 罗扬; 汪琳霞; 刘芳菊; 李悛] Hunan;[刘立; 罗扬; 汪琳霞; 刘芳菊; 李悛] 421001, China
通讯机构：
[Luo, Y.] S;School of Computer Science and Technology, University of South China, Hengyang, Hunan, China
期刊：
Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015,2015年:1975-1979 ISSN：1948-9439
通讯作者：
Wu, Daqing
作者机构：
[Wu, Daqing] Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.;[Liu, Li; Gong, XiangJian; Wu, Daqing] Univ South China, Comp Sci & Technol Inst, Hengyang 421001, Hunan, Peoples R China.;[Deng, Li; Wu, Daqing] DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.
通讯机构：
[Wu, Daqing] A;Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.
会议名称：
27th Chinese Control and Decision Conference (CCDC)
会议时间：
MAY 23-25, 2015
会议地点：
Qingdao, PEOPLES R CHINA
会议主办单位：
[Wu, Daqing] Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.^[Wu, Daqing;Liu, Li;Gong, XiangJian] Univ South China, Comp Sci & Technol Inst, Hengyang 421001, Hunan, Peoples R China.^[Wu, Daqing;Deng, Li] DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.
会议论文集名称：
Chinese Control and Decision Conference
关键词：
Multi-objective Optimization;Particle Swarm Optimizer;Neighborhood Best Particle;Dynamic Swamis;Economic Environmental Dispatch
摘要：
An efficient co-evolutionary multi-objective particle swarm optimizer named ECMPSO was proposed.ECMPSO uses dynamic multiple swarms to deal with multiple objectives,taking one objective is optimized b
作者机构：
[刘立; 伍大清] Computer Science and Technology Institute, University of South China, Hengyang, China;[伍大清] Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China;[伍大清] Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, China;[郑建国; 朱君璇; 伍大清; 赵燕] School of Business and Management, Donghua University, Shanghai, China;[伍大清] Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu, China
通讯机构：
[Huang, XinYang] U;Univ S China, Sch Comp Sci & Technol, Hengyang, Peoples R China.
会议名称：
International Workshop on Information and Electronics Engineering (IWIEE) / International Conference on Information, Computing and Telecommunications (ICICT)
会议时间：
MAR 10-11, 2012
会议地点：
Harbin, PEOPLES R CHINA
会议主办单位：
[Huang, XinYang;Tan, MinSheng;Ouyang, Lijun;Liu, Li] Univ S China, Sch Comp Sci & Technol, Hengyang, Peoples R China.
会议论文集名称：
Procedia Engineering
关键词：
DWT;Xiong invariant wavelet(XIW);rotation- and scaling- and translation-(RST) invariance;crop- invariance
摘要：
This paper presents a new method to solve the crop- invariant problem of an invariant wavelet, RSTXIW, which is a rotation- and scaling- and translation- invariant. Based on the feature points and second moments of the signal and based on a scale function of Daubiechies wavelet, we renormalize a signal in the function space. By using the feature points to divide the signal into several parts and by applying cluster analysis, we can correctly calculate the whole second-order moments of the cropped signal. Then we can obtain the crop- invariance of the renormalized signal, tested by some experiments. (C) 2011 Published by Elsevier Ltd.
