2019 IEEE 5th International Conference on Computer and Communications (ICCC)最新文献_第6页

Using Structured event to represent complaints of patients: a medical assistant for doctors 用结构化事件表示病人的抱怨:医生的医疗助理

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064234

Haowei Song, Gangmin Li, Zuopeng Liu, Xuming Bai

{"title":"Using Structured event to represent complaints of patients: a medical assistant for doctors","authors":"Haowei Song, Gangmin Li, Zuopeng Liu, Xuming Bai","doi":"10.1109/ICCC47050.2019.9064234","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064234","url":null,"abstract":"Extracting relations between entities from complaints of patients is a significant but challenging problem in intelligent medical diagnose. It can help doctors to record the main information from the complaints of patients. As the development of technologies, Doctors need a more effective and convenient way to capture entire information from patients’ complaints and build Electronic Health Records (EHRs). This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture. The event generation model adopts an open Chinese Information Extraction (open Chinese IE) and build a part-of-speech tagging to do dependency grammar analysis. Two kinds of evaluations are taken. One is metrics recall-oriented understanding for gusting evaluation (ROUGE). It measures the fitness of the generated events with the standard reference from doctors. The other are accuracy and Matthews Correlation Coefficient (MCC). They test the performance of grammar analysis. The results show our model have an excellent and robust performance.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"31 1","pages":"2193-2197"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90822531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Very Short-Term Solar Generation Forecasting Based on LSTM with Temporal Attention Mechanism 基于时间关注机制的LSTM极短期太阳能发电预测

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064298

Cheng Pan, Jie Tan, D. Feng, Yi Li

引用次数: 22

Object Detection of Optical Remote Sensing Image Based on Improved Faster RCNN 基于改进更快RCNN的光学遥感图像目标检测

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064409

Xiu Chen, Qinyu Zhang, Jize Han, Xiao Han, Y. Liu, Yuan Fang

{"title":"Object Detection of Optical Remote Sensing Image Based on Improved Faster RCNN","authors":"Xiu Chen, Qinyu Zhang, Jize Han, Xiao Han, Y. Liu, Yuan Fang","doi":"10.1109/ICCC47050.2019.9064409","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064409","url":null,"abstract":"Object detection of optical remote sensing image is an important and challenging problem. And it is widely used in the field of aerial and satellite image analysis. With the rapid increase of optical remote sensing image data and popularity of convolutional neural network, the problem has attracted lots of attention recently. However, the detection result of images with complex background is unsatisfactory, so as images with dense and small objects. Aiming at these problems, we propose a method that combined Feature Pyramid Network(FPN) and Deformable Convolution Network(DCN) to improve the Faster RCNN framework, which helps to improve the detection result. The improved network combines the low-level structural information and the high-level semantic information together to enhance the feature representation. The shared convolutional layer makes end-to-end training come true. Additionally, deformable convolution network makes feature extraction better. We adopt the proposed framework to implement experiments on DOTA dataset, attaining mean average precision(mAP)value of 0.834 on the testing dataset, which is an increase of 23% than the classic Faster RCNN.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"13 1","pages":"1787-1791"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89665741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Performance Evaluation of Scanning Support System for Constructing 3D Reconstruction Models 三维重建模型构建扫描支撑系统性能评价

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064228

Yuki Harazono, Naoya Miki, Toyohiro Higashiyama, H. Ishii, H. Shimoda, Y. Kouda

引用次数: 0

Preoperative Prediction of Prosthetic Size in Total Knee Arthroplasty Based on Multimodal Data and Deep Learning 基于多模态数据和深度学习的全膝关节置换术假体术前尺寸预测

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064325

Yu Yue, Xinguang Wang, Minwei Zhao, H. Tian, Zhiwei Cao, Qiaochu Gao, Dou Li

{"title":"Preoperative Prediction of Prosthetic Size in Total Knee Arthroplasty Based on Multimodal Data and Deep Learning","authors":"Yu Yue, Xinguang Wang, Minwei Zhao, H. Tian, Zhiwei Cao, Qiaochu Gao, Dou Li","doi":"10.1109/ICCC47050.2019.9064325","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064325","url":null,"abstract":"Total knee arthroplasty (TKA) is an effective treatment method for severe knee osteoarthritis and other knee-related diseases. Accurate match of prostheses is a crucial factor to improve the clinical efficacy and patients’ postoperative satisfaction in TKA, to which no enough attention is paid currently. In this paper, we introduce deep learning algorithm to analyze the patients’ multimodal data, such as preoperative radiograph of knees and relevant physical features (e.g. sex, height, weight), and design a software system for preoperative prediction of prosthetic type in TKA. The main processing steps include the pre-processing of X-ray images and the prediction of prosthetic type based on convolutional neural network. Research on loss function and model structure is implemented to fit the dataset better for further improvement of prediction accuracy. Transfer learning method is employed to address the problem of inadequate data. The experimental results shows that our prediction system can achieve the same accuracy level compared with that of traditional methods manipulated by experienced doctors, while it can complete the preoperative prediction automatically with lower cost and better stability.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"41 1","pages":"2077-2081"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78099038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Low Complexity Joint Detection and Decoding Algorithm for MIMO-LDPC System MIMO-LDPC系统的低复杂度联合检测与解码算法

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064293

Jiansong Miao, Yanyong Fan, Haoqiong Yin, Ningyu Chen, Weijie Li

引用次数: 1

Machine Learning Based Mm-wave 60 GHz Channel Modeling for 5G Wireless Communication Systems 基于机器学习的5G无线通信系统毫米波60 GHz信道建模

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064069

Yang Wen, Wei Hu, S. Geng, Xiongwen Zhao

引用次数: 4

A Time Series Analysis and Persistence Framework for Global Multicloud 面向全局多云的时间序列分析与持久化框架

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064422

Lu Ming, Wang Youyan, W. Lijuan, Feng Yatong

引用次数: 0

F-M II Model Realization of 2D Radar Based on Graph Theory Method 基于图论方法的二维雷达F-M II模型实现

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064343

Sheng Daoqing, Wu Xudongzi, Cheng Hua, Liu Chang

引用次数: 0

Research on Key Management Scheme of X2 Handover Protocol in LTE-R LTE-R中X2切换协议密钥管理方案研究

2019 IEEE 5th International Conference on Computer and Communications (ICCC) Pub Date : 2019-12-01 DOI: 10.1109/ICCC47050.2019.9064161

Lihua Zhang, Jiayi Bai, Panpan Jiang

引用次数: 2