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Predicting preeclampsia in early pregnancy using clinical and lab.ratory data via machine learning model

BMC Medical Informatics and Decision Making 2025年第1期25卷 1-15页

作者： Chen, Songchang Li, Jia Zhang, Xiao Xu, Wenqiu Qiu, Zhixu Yan, Siyao Zhao, Wenrui Zhao, Zhiguang Tian, Peirun Zhao, Qiang Zhang, Qun Chen, Weiping Li, Huahua Ruan, Xiaohong Xiao, Gefei Zhang, Sufen Hu, Liqing Qin, Jie Huang, Wuyan Li, Zhongzhe Wang, Shunyao Zhang, Rui Huang, Shang Wang, Xin Yao, Yao Ran, Jian Cheng, Danling Luo, Qi Pan, Teng Gao, Ruyun Zheng, Jing Wang, Yuxuan Liu, Cong Cao, Xianling Zhou, Xuanyou Xu, Naixin Zhang, Lanlan Han, Xu Wang, Haolin Feng, Suihua Li, Shuyuan Zhang, Jianguo Zhao, Lijian Wei, Fengxiang Obstetrics and Gynecology Hospital Institute of Reproduction and Development Fudan University Shanghai 200011 China Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health Clin Lab BGI Genomics Hebei Province Shijiazhuang 050011 China BGI Genomics Guangdong Province Shenzhen 518083 China Hebei Medical University Hebei Province Shijiazhuang 050011 China Department of Obstetrics and Gynecology Jiangmen Central Hospital Guangdong Province Jiangmen 529030 China Clinical Transformation and Application Key Lab for Obstetrics and Gynecology Pediatrics and Reproductive Medicine of Jiangmen Guangdong Province Jiangmen 529030 China Department of Medical Genetics and Prenatal Diagnosis Zhuhai Center for Maternal and Child Health Care Guangdong Province Zhuhai 519000 China Department of Prevention and Health Care Zhuhai Center for Maternal and Child Health Care Guangdong Province Zhuhai 519000 China Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University Hunan Province Changsha 410007 China Division of Maternal-Fetal Medicine Shenzhen Bao’ an Women’s and Children’s Hospital Guangdong Province Shenzhen 518102 China The Genetics Laboratory Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College) Longgang District Guangdong Province Shenzhen 518712 China Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College) Guangdong Province Shenzhen 518712 China Department of Blood Transfusion The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Gusu School Nanjing Medical University 80 Tianedang Road Suzhou Jiangsu 518712 China School of Basic Medical Sciences Jiamusi University Heilongjiang P

Background: This study was performed to characterize the relationship of various lab.ratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and lab.ratory data. Methods: Between January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all availab.e clinical and lab.ratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and lab.ratory data. Results: By comparing lab.ratory variables between PE patients and healthy controls, we identified 7, 18, 8, 15, 7,29 lab.ratory markers for EOPE, LOPE, and Preterm PE, severe PE, superimposed PE, first-time PE respectively. The ensemble EOPE and LOPE models incorporating clinical and lab.ratory predictors outperformed the clinical factor models respectively. The ensemble EOPE model demonstrated good sensitivity (72.22%,95% confidence interval [CI]: 57.59%-86.85%) and specificity (85.25%,95% CI: 80.54%-89.97%) in distinguishing EOPE from controls in early pregnancy. Similarly, the ensemble LOPE model showed good accuracy in differentiating LOPE from healthy participants (sensitivity: 69.57%, 95% CI: 56.27%-82.86%;specificity: 85.25%, 95% CI: 80.54%-89.97%). The prediction scores demonstrated notable positive correlations with blood pressure at admission, while they showed inverse correlations with 24-hour urine protein levels and fetal growth restriction among PE patients. In conclusion, our study identified key lab.ratory indicators for forecasting PE. The developed models exhibited good predictive capability for assessing preeclampsia risk and severity based on clinical and lab.ratory data. Clini

关键词： lab.ratory data Machine learning model Preeclampsia

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A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis

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Advanced engineering Informatics 2025年 66卷

作者： Yu, Yue Karimi, Hamid Reza Gelman, Len Liu, Xin Department of Mechanical Engineering Politecnico di Milano via La Masa 1 Milan20156 Italy School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao06600 China

Vibration-based fault diagnosis methods are widely used in industrial applications due to their high accuracy and reliability. However, their implementation is often hindered by challenges such as confined spaces, harsh environmental conditions, and cost limitations. Moreover, traditional deep learning-based approaches frequently overlook the critical relationship between virtual and physical signals, which can limit their diagnostic performance. To overcome these challenges, this study introduces a novel digital twin-driven feature imputation framework (NIFD-Net) for non-contact intelligent fault diagnosis. The NIFD-Net framework consists of three key stages: (1) codebook generation, where features from simulated vibration signals (SVS) and non-contact signals (NCS) are compressed and regularized using diagnosis-oriented codebooks;(2) mapping construction, where a Transformer is employed to establish the relationship between SVS and NCS in a compressed feature space;and (3) diagnostic enhancement, where imputed SVS features are integrated with original NCS features to improve diagnostic performance. First, digital twin (DT)-enabled models, developed using structural parameters and fault characteristics, dynamically simulate the operating conditions of rolling bearings to generate high-quality SVS samples. The relationship between the two feature spaces of SVS and NCS is then learned by a transformer to impute the SVS features. Finally, the imputed SVS features are integrated with the original NCS features to enhance fault diagnostic performance. Extensive experiments on three case studies demonstrate that NIFD-Net outperforms state-of-the-art methods, achieving an average accuracy of 98.34% across diverse tasks. Notably, the framework exhibits strong noise robustness, maintaining high diagnostic performance even under adverse conditions (e.g., SNR = -10 dB). The results highlight the effectiveness of integrating simulated and real signals for non-contact fault diag

关键词： Vibrations (mechanical)

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Robust Iterative Learning control of Single-phase Grid-connected Inverter

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International Journal of Automation and computing 2014年第4期11卷 404-411页

作者： Zhong-Qiang Wu Chun-Hua Xu Yang Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

For the single phase inductance-capacitance-inductance(LCL) grid-connected inverter in micro-grid, a kind of robust iterative learning controller is designed. Based on the output power droop characteristics of inverter, the current sharing among the inverters is achieved. Iterative learning strategy is suitable for repeated tracking control and inhibiting periodic disturbance, and is designed using robust performance index, so that it has the ability to overcome the uncertainty of system parameters. Compared with the repetitive control, the robust iterative learning control can get high precision output waveform, and enhance the tracking ability for waveform, and the distortion problem of the output signal can be solved effectively.

关键词： Inductance-capacitance-inductance (LCL) filter iterative learning robust control power droop-characteristics repetitive control

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Fuzzy PID control of Space Manipulator for Both Ground Alignment and Space Applications

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International Journal of Automation and computing 2014年第4期11卷 353-360页

作者： Fu-Cai Liu Li-Huan Liang Juan-Juan Gao Key Lab of Industrial Computer Control of Hebei Province Yanshan University

Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential(PID)control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments. The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller. Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem. Compared with the results of conventional PID control,the control performance of the fuzzy PID is more effective for manipulator trajectory control.

关键词： Space manipulator trajectory tracking ground alignment space applications fuzzy proportional-integral-differential (PID) gravity environment

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Multi-Objective Optimization for Tandem Cold Rolling Schedule

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Journal of Iron and Steel Research International 2010年第11期17卷 34-39页

作者： YANG Jing-ming ZHANG Qing CHE Hai-jun HAN Xin-yan Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei China

Considering the multivariable, strong-coupled, and nonlinear features of tandem cold rolling mill, a mathematical model of multi-objective optimization was built to facilitate the design of new systems aiming at equating the relative load, preventing slip, and obtaining best shape. BP （back propagation） neural network based on Levenberg- Marquardt algorithm was used for predicting the rolling force. The multi-objective fuzzy theory and method were introduced during the optimization. With an example of 1 370 mm tandem cold rolling mill, the rolling schedule of the common rolling, the single-objective optimization design, and the multi-objective fuzzy optimization design were compared with each other. The results generated from the case study showed that the proposed approach could significantly decreased the values of three objective functions simultaneously and the performance of the optimal rolling schedule was satisfactory and promising. Moreover, the capability and usefulness of fuzzy application in tandem cold rolling schedule were clearly demonstrated.

关键词： rolling schedule tandem cold rolling neural network multi-objective fuzzy optimization

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A novel multi-modal tactile sensor design using thermochromic material

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Science China(Information Sciences) 2019年第11期62卷 189-191页

作者： Fuchun SUN Bin FANG Hongxiang XUE Huaping LIU Haiming HUANG Department of Computer Science and Technology Tsinghua University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University College of Information Engineering Shenzhen University

Tactile sensors are important for ensuring robotic perception [1], and their performance directly affects a robot’s capability for performing dexterous operations. These sensors mimic the tactile sensing capability of human hands. This is because the skin of human hands is involved in considerably meticulous works and can provide multi-

关键词： A novel multi-modal tactile sensor design using thermochromic material

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Distributed containment control for multi-robot system based on binocular vision and RBF neural network

International Journal of Control and Automation

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International Journal of control and Automation 2015年第9期8卷 139-152页

作者： Shao, Nuan Li, Huiguang Liu, Le Li, Guoyou Key Lab of Industrial Computer Control Engineering of Hebei Province College of Electrical Engineering Yanshan University Qinhuangdao China

Aiming at the distributed containment control problem for Multi-Robot system with dynamic leaders, we use binocular vision as the sensing device, so firstly, a binocular visual model for dynamic target is established by introducing the pseudo depth variable, and it is used to provide Real-Time status information for the followers. Then, in the case that each follower can only get information from part of the leaders, even individual follower can not get any information from the leaders directly, the topological relationship among robots is built by using the graph theory. Once more, RBF neural networks are employed to approximate the uncertainties in the system model of followers, and L2robust control laws are used to restrain the influences of the approximation errors. Finally, a simulation is carried out on multiple 2 Degrees-Of-Freedom (DOF) robots, and results validate the designed controllers can drive the followers converge to the convex hull spanned by the leaders asymptotically and realize synchronization motion ultimately. © 2015 SERSC.

关键词： Multipurpose robots

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Dynamics Modeling and Robust Trajectory Tracking control for a Class of Hybrid Humanoid Arm Based on Neural Network

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Chinese Journal of Mechanical engineering 2009年第3期22卷 355-363页

作者： WANG Yueling JIN Zhenlin College of Mechanical Engineering Yanshan University Qinhuangdao 066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm（HHA）, which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network（NN）-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function（RBF） NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.

关键词： hybrid humanoid arm dynamic modeling neural network adaptive control trajectory tracking

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Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

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控制理论与应用（英文版） 2011年第4期9卷 541-547页

作者： Hongbin WANG Yan WANG Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao Hebei 066004 China

Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely availab.e, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.

关键词： Iterative learning control State-delay system Initial condition Uncertain disturbances

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Adaptive finite time consensus control of multiple mechanical systems under a switching graph 35

Adaptive finite time consensus control of multiple mechanica...

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第35届中国控制会议

作者： WANG Hongbin ZHOU Zhen WANG Yueling Key Lab of Industrial Computer Control Engineering of Hebei Province Yansnan University

ISBN: (纸本)9781509009107

In this paper,adaptive finite time consensus control problem is investigated for multiple mechanical systems with *** adaptive distributed control algorithms are proposed for the multiple mechanical systems under a switching undirected graph,which is addressed through a novel Markov *** any detailed information of the system model,the adaptive distributed controller has a faster and higher precision tracking *** is shown that the states of the mechanical systems can reach a consensus within finite *** finally,an illustrative simulation on the multiple robotic manipulators is given to demonstrate the effectiveness of the proposed controller.

关键词： Finite time consensus Adaptive control Multiple mechanical system Markov chain

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