Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients by Zhenyi Liu, Yihao Huang, Long Li, Yisha Xu, Peng Wu, Zhigang Zhang, Tingyong Han, Liangjie Zhang, Ming Zhang

“…ObjectiveTo address the limitations of conventional delirium prediction models in emergency polytrauma care, this study developed an interpretable machine learning (ML) framework incorporating trauma-specific biomarkers and advanced optimization algorithms for risk stratification of delirium in emergency polytrauma patients.MethodsThis multi-center retrospective observational cohort study was conducted across six hospitals in the Ya’an region. …”
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Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model by Gang Li, Ruijiang Ran, Jun Fang, Hao Peng, Shengmin Wang

“…Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…The researcher suggests increasing the data size, as it is possible to improve the accuracy of models by increasing the data size. …”
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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Evaluation on the VisDrone2019 dataset indicates that PARE-YOLO achieves a 5.9% improvement in mean Average Precision (mAP) at a threshold of 0.5, compared to the original YOLOv8 model. …”
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A Recognition Method for Adzuki Bean Rust Disease Based on Spectral Processing and Deep Learning Model by Longwei Li, Jiao Yang, Haiou Guan

“…Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. …”
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AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. …”
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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…A key advantage of these hybrid ET models is their improved performance, particularly under extreme conditions, compared to ET estimates relying solely on ML. …”
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Adaptive Feedforward Vibration Control of Helicopter Cabin Floor Driven by Piezoelectric Stack Actuators: Modeling, Simulation and Experiments by Laishou Song, Yingquan Wang, Xiaoyu Shen

“…A scale helicopter airframe model, preserving the local geometric similarity of the cabin floor structure, is developed and optimized to capture the low-order global dynamic characteristics of a reference airframe. …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
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A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling by Ahmad Almadhor, Shtwai Alsubai, Natalia Kryvinska, Nejib Ghazouani, Belgacem Bouallegue, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…Abstract The growing need for energy efficiency in buildings has driven significant improvements in digitalisation and intelligent energy management. …”
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Enhancing Pollen Prediction in Beijing, a Chinese Megacity: Leveraging Ensemble Learning Models for Greater Accuracy by Wenxi Ruan, Ziming Li, Zhaobin Sun, Xingqin An, Yuxin Zhao, Shuwen Zhang, Yinglin Liang, Yaqin Bu, Jingyi Xin, Xiaoyi Hang

“…The Weighted Ensemble model, which adjusts other models based on weighted optimization to mitigate excessive peaks, consistently yields stable results with an R2 exceeding 0.67. …”
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Distributed Coordinated Dispatch Model for Multi-area Interconnected Integrated Energy Systems Based on Sequential Cone Programming by Yujie REN, Yuhan HUANG, Zhenbo WEI

“…The solution of the distributed algorithm based on ATC is close to the global optimal solution of the distributed algorithm. …”
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A PSO-XGBoost Model for Predicting the Compressive Strength of Cement–Soil Mixing Pile Considering Field Environment Simulation by Jiagui Xiong, Yangqing Gong, Xianghua Liu, Yan Li, Liangjie Chen, Cheng Liao, Chaochao Zhang

“…A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. …”
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A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…The physical and mechanical properties of the soil–structure interface under the freeze–thaw condition are complex, making empirical shear strength models poorly applicable. This study employs integrated machine learning algorithms to model the shear behavior of frozen–thawed silty clay and the structure interface. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The objective is to identify predictors of dropout and enhance intervention strategies.MethodsThe study utilized seven base machine learning algorithms to create a stacked ensemble model with three meta-learners: Random Forest (RF), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost). …”
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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

“…Gender and the top 30 variables with the highest coefficient of determination () in explaining the variance in NCD status were retained for model construction. Subsequently, the optimal network structure was identified using the Tabu search algorithm guided by Bayesian Information Criterion, with parameters estimated by maximum likelihood estimation. …”
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Predicting Quality of Life in People Living with HIV: A Machine Learning Model Integrating Multidimensional Determinants by Meilian Xie, Zhiyun Zhang, Yanping Yu, Li Zhang, Jieli Zhang, Dongxia Wu

“…We therefore aimed to develop and validate machine learning models that predict QoL trend in PLWH, identifying key determinants to inform personalized interventions and optimize long-term well-being. …”
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Research on primary frequency regulation and VSC-HVDC frequency synchronization control coordination method for large-scale hydropower DC export regional grid by Hongchun Shu, Huaibin Gao, Zongxue Shao, Yongzan Cui, Wei Zhao

“…An evolutionary algorithm is used to solve the optimal parameters hierarchically progressively. …”
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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. …”
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Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA by Tong Y, Wen K, Li E, Ai F, Tang P, Wen H, Guo B

“…Yangyang Tong,1 Kuo Wen,2 Enguang Li,3 Fangzhu Ai,4 Ping Tang,5 Hongjuan Wen,3 Botang Guo5 1Department of Pulmonary Oncology, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 2College of Traditional Chinese Medicine, Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 3College of Health Management, Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 4School of Nursing, Jinzhou Medical University, Jinzhou, Liaoning Province, 121000, People’s Republic of China; 5Department of General Practice, the Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, 518001, People’s Republic of ChinaCorrespondence: Botang Guo, Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, 518001, People’s Republic of China, Email hmugbt@hrbmu.edu.cn Hongjuan Wen, College of Health Management, Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China, Email wenhongjuan2004@163.comObjective: The aim of this study was to establish a risk prediction model for sleep quality in patients with obstructive sleep apnea (OSA) based on machine learning algorithms with optimal predictive performance.Methods: A total of 400 OSA patients were included in this study. …”
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