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intelligent prediction of RBC demand in trauma patients using decision tree methods

Military Medical Research 2022年第2期9卷 152-163页

作者： Yan-Nan Feng Zhen-Hua Xu Jun-Ting Liu Xiao-Lin Sun De-Qing Wang Yang Yu Department of Transfusion Medicine the First Medical Center of Chinese PLA General HospitalNo.28Fuxing Rd.Beijing 100853China Beijing Hexing Chuanglian Health Technology Co. Ltd.Beijing 100176China

Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurately *** this study,a machine learning decision tree algorithm[classification and regression tree(CRT)and eXtreme gradient boosting(XGBoost)]was proposed for the demand prediction of traumatic blood transfusion to provide technical support for ***:A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma *** vital signs,laboratory examination parameters and blood transfusion volume were used as variables,and the non-invasive parameters and all(non-invasive+invasive)parameters were used to construct an intelligent prediction model for red blood cell(RBC)demand by logistic regression(LR),CRT and *** prediction accuracy of the model was compared with the area under curve(AUC).Results:For non-invasive parameters,the LR method was the best,with an AUC of 0.72[95%confidence interval(CI)0.657–0.775],which was higher than the CRT(AUC 0.69,95%CI 0.633–0.751)and the XGBoost(AUC 0.71,95%CI 0.654–0.756)(Pprediction *** all the prediction parameters,XGBoost was the best,with an AUC of 0.94(95%CI 0.893–0.981),which was higher than the LR(AUC 0.80,95%CI 0.744–0.850)and the CRT(AUC 0.82,95%CI 0.779–0.853)(Pprediction ***:The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR *** can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue envir

关键词： Mathematical model intelligent prediction Decision tree Non-invasive parameters Invasive parameters Trauma Transfusion

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis intelligent prediction Improved CNN

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Measurement for Water Content in Oil-Water Two Phase Flow Based on Novel Hybrid intelligent prediction Model

Measurement for Water Content in Oil-Water Two Phase Flow Ba...

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第二十六届中国控制会议

作者： Zhang Dongzhi,Xia Bokai,Fu Tao College of Information and Control Engineering,China University of Petroleum(East China),Dongying 257061,P.R.China

Some parameters affecting the measurement of water content in oil/water two-phase flow are detected using multi-sensor technology,and a novel hybrid intelligent prediction model is proposed to improve measuring precision of water *** advanced information processing technologies,such as neural networks optimized by hybrid genetic algorithm,combined method for decisions of multiple sub-modules,are introduced in this intelligent prediction model,which guarantee a good prediction effect with global and fast convergence,strong generalization capability and high *** research re-sult in this paper indicates that prediction precision is improved to a great extent in the all-round measuring range for water content,while the development cost is at a low *** is a new and effective method for measuring water content in oil/water two-phase flow.

关键词： Two-phase Flow Multi-sensor Neural Network intelligent prediction

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THE EXPERIMENT RESEARCH OF FORCE CONTROL BASED ON intelligent prediction IN UNKNOWN ENVIRONMENT

THE EXPERIMENT RESEARCH OF FORCE CONTROL BASED ON INTELLIGEN...

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2006年中国机械工程学会年会暨中国工程院机械与运载工...

作者： WANG Lei*, HAO Yongping*,WANG Fei *R&D Center of CAD/CAM Technology, Shenyang Ligong University, Shenyang 110168, China School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110004, China

An intelligent prediction algorithm arising from conventional force control models is developed to deal with a force-controlled execution of compliant robot tasks in an unknown environment. The algori

关键词： Robot force control intelligent prediction unknown environment fuzzy control.

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intelligent Predictive Analysis of the Performance Level of Discipline Construction in Local Universities Based on Grey prediction

Intelligent Predictive Analysis of the Performance Level of ...

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作者： Limei Liu Xiuling Li School of Applied Mathematics Jilin University of Finance and Economics

In order to overcome the difficulties in measurement data collecting of the performance level of discipline construction in local universities,GM（1,1） grey prediction model was used to predict and analyze the performance level of discipline construction in local universities,which combined with the general performance indicator system of discipline *** research shows that to improve the performance level of discipline construction,we must strengthen the construction of teaching staff,ensure sufficient and high-quality teaching resources,optimize the process of personnel training,promote the development of students,strengthen quality supervision and guarantee,enhance the scientific research ability and awareness of teachers and students,clarify the orientation and goals of universities and actively promote the economic and social development of the country and the region.

关键词： Discipline Construction Local Universities Performance Level Grey prediction intelligent prediction

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Longwall face roof disaster prediction algorithm based on data model driving

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International Journal of Coal Science & Technology 2022年第1期9卷 151-166页

作者： Yihui Pang Hongbo Wang Jinfu Lou Hailong Chai Coal Mining Research Institute Co. Ltd of CCTEGBeijing 100013China Coal Mining and Designing Department Tiandi Science&Technology Co.LtdBeijing 100013China Coal Mining Branch China Coal Research InstituteBeijing 100013China School of Energy and Mining Engineering China University of Mining and Technology-BeijingBeijing 100083China

Hydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining *** load,location,and attitude of the hydraulic support are important sets of basis data to predict roof *** paper summarized and analyzed the status of coal mine safety accidents and the primary influencing factors of roof *** work also proposed monitoring characteristic parameters of roof disasters based on support posture-load changes,such as the support location and support *** data feature decomposition method of the additive model was used with the monitoring load data of the hydraulic support in the Yanghuopan coal mine to effectively extract the trend,cycle period,and residuals,which provided the period weighting characteristics of the longwall *** autoregressive,long-short term memory,and support vector regression algorithms were used to model and analyze the monitoring data to realize single-point *** seasonal autoregressive integrated moving average(SARIMA)and autoregressive integrated moving average(ARIMA)models were adopted to predict the support cycle load of the hydraulic *** SARIMA model is shown to be better than the ARIMA model for load predictions in one support cycle,but the prediction effect of these two algorithms over a fracture cycle is ***,we proposed a hydraulic support load prediction method based on multiple data cutting and a hydraulic support load template *** constructed technical framework of the roof disaster intelligent prediction platform is based on this method to perform predictions and early warnings of roof disasters based on the load and posture monitoring information from the hydraulic support.

关键词： Data model Roof disaster Hydraulic support Characteristic parameter intelligent prediction

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A study on predicting crisis information dissemination in epidemic-level public health events

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Journal of Safety Science and Resilience 2023年第3期4卷 253-261页

作者： Lin Zhang Xin Wang Jinyu Wang Ping Yang Peiling Zhou Ganli Liao School of Economics and Management Beijing Information Science and Technology UniversityBeijing 100192China Beijing Academy of Science and Technology Beijing 100089China

Crisis information dissemination plays a key role in the development of emergency responses to epidemic-level public health ***,clarifying the causes of crisis information dissemination and making accurate predictions to effectively control such situations have attracted extensive *** on media richness theory and persuasion theory,this study constructs an index system of crisis information dissemination impact factors from two aspects:the crisis information publisher and the published crisis information content.A multi-layer perceptron is used to analyze the weight of the index system,and the prediction is transformed into a pattern classification problem to test crisis information *** this study,COVID-19 is considered a representative *** experiment is conducted to predict the crisis information dissemination of COVID-19 in two *** related to COVID-19 from these two megacities are acquired from the well-known Chinese social media platform *** experimental results show that not only the identity but also the social influence of the information publisher has a significant impact on crisis information dissemination in epidemic-level public health ***,the proposed model achieves more than 95%test accuracy,precision rate,recall value and f1-score in the prediction *** study provides decision-making support for government departments and a guide for correctly disseminating crisis information and public opinion during future epidemic-level public health events.

关键词： Epidemic-level public health events COVID-19 Crisis information dissemination Machine learning intelligent prediction

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prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1380-1397页

作者： Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou Manoj Khandelwal School of Resources and Safety Engineering Central South UniversityChangsha410083China Universidad Politecnica de Madrid-ETSI Minas y Energia Rios Rosas 21Madrid28003Spain College of Locomotive and Rolling Stock Engineering Dalian Jiaotong UniversityDalian116028China Department of Mathematics University of California Santa BarbaraSanta BarbaraCA93106USA State Key Laboratory of Safety and Health for Metal Mines Maanshan243000China School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratVIC3350Australia

The main purpose of blasting operation is to produce desired and optimum mean size rock *** or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production ***,accurate prediction of rock fragmentation is crucial in blasting *** fragment size(MFS) is a crucial index that measures the goodness of blasting *** the past decades,various models have been proposed to evaluate and predict blasting *** these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential *** this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,*** search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the *** prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI *** all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting *** types of mathematical indices,*** square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction *** R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing ***,sensitivity analysis is performed to understand the influence of input parameters on *** shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

关键词： Blasting mean fragment size e-support vector regression(e-SVR) V-support vector regression(v-SVR) Meta-heuristic algorithms intelligent prediction

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intelligent predicting approach of peritoneal fluid absorption rate based-on neural network

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控制理论与应用（英文版） 2003年第1期1卷 82-85页

作者： MeiZHANG YuemingHU TaoWANG InstituteofAutomationScienceandEngineering SouthChinaUniversityofTechnologyGuangzhouGuangdong510640China InstituteofNephrology PekingUnivemityFirstHospitalBeijing100034China

This paper addresses the important intelligent predicting problem of peritoneal absorption rate in the peritoneal dialysis treatment process of renal failure. As the index of dialysis adequacy, KT/V and Ccr are widely used and accepted. However, growing evidence suggests that the fluid balance may play a critical role in dialysis adequacy and patient outcome. Peritoneal fluid absorption decreases the peritoneal fluid removal. Understanding the peritoneal fluid absorption rate will help clinicians to optimize the dialysis dwell time. The neural network approach is applied to the prediction of peritoneal absorption rate. Compared with multivariable regression method, the experimental results showed that neural network method has an advantage over multivariable regression. The application of this predicting method based-on neural network in clinic is instructive. Keywords Peritoneal dialysis - Neural network - intelligent prediction - Peritoneal absorption This work was supported in part by the guangdong Province Scientific and Technological key Research Program (No.2002C3021l) and the South China University of Technology.

关键词： Peritoneal dialysis Neural network intelligent prediction Peritoneal absorption

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