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Beyond the blank page:Frequentist and Bayesian perspectives on risk prediction algorithms 认领 引用
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作者 Francisco Tustumi Felipe Antonio Boff Maegawa Pedro Luiz Serrano Uson Junior 《World Journal of Gastrointestinal Oncology》 SCIE 2025年第12期337-341,共5页
Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(M... Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(ML)models are increasingly being applied to predict outcomes,highlighting the growing significance of data-driven methods for clinical decision-making.Currently,frequentist approaches dominate prediction models,including most ML algorithms;these rely exclusively on observed datasets and risk overlooking the cumulative value of prior clinical knowledge.In contrast,Bayesian reasoning formally integrates existing evidence with new data.In this letter,we examine the strengths of frequentist-based prediction models,discuss how Bayesian methods may improve predictive accuracy,and argue that combining both approaches offers a promising path toward more robust,interpretable,and clinically useful prediction tools in surgery.This integration can yield robust,interpretable,and clinically relevant tools that advance personalized surgical care. 展开更多
关键词 Gastric cancer Bayes theorem Artificial intelligence Probability learning Prediction algorithms Risk
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A New Adaptive Prediction Algorithm for Judicial Sentencing with Empirical Studies 认领 引用
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作者 DAI Ruifen WANG Fang GUO Lei 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2025年第1期3-20,共18页
With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing predictio... With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods. 展开更多
关键词 Adaptive prediction algorithm judicial mechanism neural networks sentencing prediction
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A Novel Vertical Handoff Algorithm Based on Fuzzy Logic in Aid of Grey Prediction Theory in Wireless Heterogeneous Networks 认领 引用 被引量：2
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作者 刘侠 蒋铃鸽 《Journal of Shanghai Jiaotong university(Science)》 EI 2012年第1期25-30,共6页
To coordinate the various access technologies in the 4G communication system,intelligent vertical handoff algorithms are required.This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy... To coordinate the various access technologies in the 4G communication system,intelligent vertical handoff algorithms are required.This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy logic with the aid of grey theory and dynamic weights adaptation.The grey prediction theory(GPT) takes 4 sampled received signal strengths as input parameters,and calculates the predicted received signal strength in order to reduce the call dropping probability.The fuzzy logic theory based quantitative decision algorithm takes 3 quality of service(QoS)metric,received signal strength(RSS),available bandwidth(BW),and monetary cost (MC)of candidate networks as input parameters.The weight of each QoS metrics is adjusted along with the networks changing to trace the network condition.The final optimized vertical handoff decision is made by comparing the quantitative decision values of the candidate networks.Simulation results demonstrate that the proposed algorithm provides high performance in heterogeneous as well as homogeneous network environments. 展开更多
关键词 fuzzy logic theory grey prediction algorithm quantitative decision vertical handoff
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Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm 认领 引用 被引量：8
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作者 F. Nakhaei M.R. Mosavi A. Sam 《International Journal of Mining Science and Technology》 EI CAS 2013年第1期69-77,共9页
Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral proce... Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error. 展开更多
关键词 Artificial neural network Genetic algorithm Flotation column Grade Recovery Prediction
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Advances of Bioinformatics Tools Applied in Virus Epitopes Prediction 认领 引用 被引量：8
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作者 Simon Rayner 《Virologica Sinica》 CAS CSCD 2011年第1期1-7,共7页
In recent years,the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein,a general over... In recent years,the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein,a general overview of different tools currently available,including T cell and B cell epitopes prediction tools,is presented. And the principles of different prediction algorithms are reviewed briefly. Finally,several examples are present to illustrate the application of the prediction tools. 展开更多
关键词 Epitope Bioinformatics Epitope prediction algorithms
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Carbon Emission Factors Prediction of Power Grid by Using Graph Attention Network 认领 引用 被引量：4
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作者 Xin Shen Jiahao Li +3 位作者 YujunYin Jianlin Tang Weibin Lin Mi Zhou 《Energy Engineering》 EI 2024年第7期1945-1961,共17页
Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calcul... Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%. 展开更多
关键词 Predict carbon factors graph attention network prediction algorithm power grid operating parameters
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Development,validation,and transportability of several machine-learned,non-exercise-based VO2maxprediction models for older adults 认领 引用 被引量：1
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作者 Benjamin T.Schumacher Michael J.LaMonte +5 位作者 Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar 《Journal of Sport and Health Science》 SCIE CAS CSCD 2024年第5期611-620,共10页
Background:There exist few maximal oxygen uptake(VO2max)non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO2maxis infeas... Background:There exist few maximal oxygen uptake(VO2max)non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO2maxis infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO2maxprediction algorithms.Methods:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2maxtests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO2maxvalues.We developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging cohorts.Finally,we quantified the associations between measured and predicted VO2maxand mortality.Results:The age was 69.0±10.4 years(mean±SD)and the measured VO2maxwas 21.6±5.9 mL/kg/min.Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,respectively.Incremental quartiles of measured VO2maxshowed an inverse gradient in mortality risk.Predicted VO2maxvariables yielded similar effect estimates but were not robust to adjustment.Conclusion:Measured VO2maxis a strong predictor of mortality.Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment.Future studies should seek to reproduce these results so that VO2max,an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging. 展开更多
关键词 Cardiorespiratory fitness Prediction algorithms Epidemiology Mortality
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Nonlinear model predictive control based on support vector machine and genetic algorithm 认领 引用 被引量：16
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作者 冯凯 卢建刚 陈金水 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期2048-2052,共5页
This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ... This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection. 展开更多
关键词 Support vector machine Genetic algorithm Nonlinear model predictive control Neural network Modeling
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Ship motion extreme short time prediction of ship pitch based on diagonal recurrent neural network 认领 引用 被引量：3
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作者 SHEN Yan XIE Mei-ping 《Journal of Marine Science and Application》 2005年第2期56-60,共5页
A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The prin... A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible. 展开更多
关键词 extreme short time prediction diagonal recursive neural network recurrent prediction error learning algorithm unbiasedness
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Employment of predictive search algorithm in digital image correlation 认领 引用
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作者 马志峰 王昊 韩福海 《Journal of Beijing Institute of Technology》 EI CAS 2014年第2期254-259,共6页
A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference ... A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring. 展开更多
关键词 machine vision predictive search algorithm digital image correlation sub-pixel displacement measurement
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Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet 认领 引用
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作者 Crépin Foké Jean-Pierre Kenné Ngongang Somen Bill Diego 《Journal of Transportation Technologies》 2023年第1期1-17,共17页
The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to pr... The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values. 展开更多
关键词 Maintenance 4.0 Digital Technologies Failureprediction Artificial Intelligence Artificial Intelligence Prediction Algorithm
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Prediction-based protocol for mobile target tracking in wireless sensor networks 认领 引用 被引量：3
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作者 Liang Xue Zhixin Liu Xinping Guan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第2期347-352,共6页
Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce... Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm （ESPA） is presented. With the aid of an additive proportion and differential （PD） controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius （OSR） algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively. 展开更多
关键词 wireless sensor network target tracking protocol predictive algorithm recovery mechanism.
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Application of Predictive Algorithm in Head-Tracking System 认领 引用
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作者 姜明 李科杰 李鑫 《Journal of Beijing Institute of Technology》 EI CAS 2002年第3期306-310,共5页
The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model for... The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model for the telerobotic scout system is built. In respect of eliminating error caused by time delay and making reasonable prediction to the data stream, many methods are experimented in order to realize the aim of real time tracking. The application of extrapolation algorithm and auto recursive algorithm in the orientation tracking is described in detail. These two algorithms are realized in Matlab environment. Through analysis of the curves generated by using these two predictive algorithms, an appropriate method was applied in the telerobotic scout system. The effect is satisfying. 展开更多
关键词 teleoperation head tracking predictive algorithm
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Application of nine point logic control and predictive pid control algorithms in smart grid decision-making 认领 引用
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作者 Zhixin Ou 《Journal of Highway and Transportation Research and Development(English Edition)》 2024年第4期8-15,共8页
The smart grid operation and decision-making system has the characteristics of smart power detection,data cloud storage,and information fusion.The disadvantage is that the difficulty of model estimation is high,signif... The smart grid operation and decision-making system has the characteristics of smart power detection,data cloud storage,and information fusion.The disadvantage is that the difficulty of model estimation is high,significant fluctuations in energy storage and transmission,algorithm update iteration delay,and low accuracy of data fusion technology.First,models and algorithms for smart grids,improve the multiple domain value delay problem of output stability.Second,the nine-point logic control strategy is based on the partition of deviation and deviation variation based on pan Boolean operations,effectively solving the frequency and band amplitude issues of power grid transmission load fluctuations.When the integral variable or input signals change,compared with traditional PiD control,it is concluded that,just adjusting according to the nine-point logic control strategy,can obtain ideal control and output results.Finally,establishing a simulation model,the results indicate that its control curve performs well in model matching,satisfactory operation in case of model mismatch,and ensures that the fluctuation amplitude and error accuracy meet the requirements.The control strategy has the characteristics of a fast output response,high stability and robustness,and a small overshoot for changing power grid models. 展开更多
关键词 intelligent transport smart grid decision-making predictive PiD control algorithm nine-point logic control multi-domain fluctuation experiment/Simulation
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NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS 认领 引用
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作者 Tian Sheping Ding Guoqing +1 位作者 Yan Detian Lin Liangming Department of Information Measurement and Instrumentation,Shanghai Jiaotong University,Shanghai 200030, China 《Chinese Journal of Mechanical Engineering》 EI CAS 2004年第2期306-310,共5页
The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is... The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme. 展开更多
关键词 Artificial muscle Neural networks Recursive prediction error algorithm Nonlinear modeling and controlling
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A NEW TECHNIQUE FOR PREDICTING DISTRIBUTION OF TERRESTRIAL VERTEBRATES USING INFERENTIAL MODELING 认领 引用 被引量：2
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作者 陈国君 A.Townsend Peterson 《Zoological Research》 2000年第3期231-237,共7页
A new technique for predicting species' geographic distribution is described.The approach involves 3 steps:①setting up geographic base data;②collecting and georeferencing distributional points;③modeling ecologi... A new technique for predicting species' geographic distribution is described.The approach involves 3 steps:①setting up geographic base data;②collecting and georeferencing distributional points;③modeling ecological niches using the biodiversity species workshop implementation of the genetic algorithm for rule set prediction (GARP).To illustrate these procedures,an example based on the Brown Eared Pheasant (Crossoptilon mantchuricum) is developed.This technique constitutes a useful tool for assessing geographic distribution for questions of ecology,biogeography,systematics,and conservation biology. 展开更多
关键词 Geographic information systems Genetic algorithm for rule set prediction Distribution Ecological niche
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An improved algorithm for noise-robust sparse linear prediction of speech 认领 引用 被引量：1
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作者 ZHOU Bin ZOU Xia ZHANG Xiongwei 《Chinese Journal of Acoustics》 CSCD 2015年第1期84-95,共12页
The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear p... The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications. 展开更多
关键词 An improved algorithm for noise-robust sparse linear prediction of speech PESQ LP
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Long-term autonomous time-keeping of navigation constellations based on sparse sampling LSTM algorithm 认领 引用 被引量：3
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作者 Shitao Yang Xiao Yi +5 位作者 Richang Dong Yifan Wu Tao Shuai Jun Zhang Qianyi Ren Wenbin Gong 《Satellite Navigation》 SCIE EI CSCD 2024年第3期208-221,共14页
The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations.This operational mode relies heavily on the support of ground stations.... The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations.This operational mode relies heavily on the support of ground stations.To enhance the high-precision autonomous timing capability of next-generation navigation satellites,it is necessary to autonomously generate a comprehensive space-based time scale on orbit and make long-term,high-precision predictions for the clock error of this time scale.In order to solve these two problems,this paper proposed a two-level satellite timing system,and used multiple time-keeping node satellites to generate a more stable space-based time scale.Then this paper used the sparse sampling Long Short-Term Memory(LSTM)algorithm to improve the accuracy of clock error long-term prediction on space-based time scale.After simulation,at sampling times of 300 s,8.64×104 s,and 1×106 s,the frequency stabilities of the spaceborne timescale reach 1.35×10-15,3.37×10-16,and 2.81×10-16,respectively.When applying the improved clock error prediction algorithm,the ten-day prediction error is 3.16×10-10 s.Compared with those of the continuous sampling LSTM,Kalman filter,polynomial and quadratic polynomial models,the corresponding prediction accuracies are 1.72,1.56,1.83 and 1.36 times greater,respectively. 展开更多
关键词 Autonomous timekeeping Time scale algorithm Clock error prediction algorithm LSTM
Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model 认领 引用 被引量：2
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作者 姚才 金龙 赵华生 《Acta meteorologica Sinica》 2009年第6期701-712,共12页
After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon ... After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction. 展开更多
关键词 monsoon index ensemble prediction genetic algorithm neural network mean generating function
Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients:Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model 认领 引用
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作者 Rong Fan Ya-Ru Shi +24 位作者 Lei Chen Chuan-Xin Wang Yun-Song Qian Yan-Hang Gao Chun-Ying Wang Xiao-Tang Fan Xiao-Long Liu Hong-Lian Bai Dan Zheng Guo-Qing Jiang Yan-Long Yu Xie-Er Liang Jin-Jun Chen Wei-Fen Xie Lu-Tao Du Hua-Dong Yan Yu-Jin Gao Hao Wen Jing-Feng Liu Min-Feng Liang Fei Kong Jian Sun Sheng-Hong Ju Hong-Yang Wang Jin-Lin Hou 《Journal of Clinical and Translational Hepatology》 SCIE CSCD 2025年第9期743-753,共11页
Background and Aims:Given the high burden of hepatocellular carcinoma(HCC),risk stratification in patients with cirrhosis is critical but remains inadequate.In this study,we aimed to develop and validate an HCC predic... Background and Aims:Given the high burden of hepatocellular carcinoma(HCC),risk stratification in patients with cirrhosis is critical but remains inadequate.In this study,we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography(CT)images into the established age-male-ALBI-platelet(aMAP)clinical model.Methods:Patients were enrolled between 2018 and 2023 from a Chinese multicenter,prospective,observational cirrhosis cohort,all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment.The aMAP clinical score was calculated,and radiomic(PyRadiomics)and deep learning(ResNet-18)features were extracted from liver and spleen regions of interest.Feature selection was performed using the least absolute shrinkage and selection operator.Results:Among 2,411 patients(median follow-up:42.7 months[IQR:32.9–54.1]),118 developed HCC(three-year cumulative incidence:3.59%).Chronic hepatitis B virus infection was the main etiology,accounting for 91.5%of cases.The aMAP-CT model,which incorporates CT signatures,significantly outperformed existing models(area under the receiver-operating characteristic curve:0.809–0.869 in three cohorts).It stratified patients into high-risk(three-year HCC incidence:26.3%)and low-risk(1.7%)groups.Stepwise application(aMAPaMAP-CT)further refined stratification(three-year incidences:1.8%[93.0%of the cohort]vs.27.2%[7.0%]).Conclusions:The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures,enabling precise identification of high-risk cirrhosis patients.This approach personalizes surveillance strategies,potentially facilitating earlier detection and improved outcomes. 展开更多
关键词 Hepatocellular carcinoma Liver cirrhosis Radiomics Deep learning Machine learning Prediction algorithms
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