Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

“…Logistic and Cox regression analyses, Kaplan–Meier (KM) survival analysis, restricted cubic spline (RCS) analysis, and mediation effect analysis were employed to investigate the association between age, RDW levels, and 180-day and 1-year mortality in GCA patients hospitalized or admitted to the ICU. Predictive models were constructed using machine learning algorithms, and SHapley Additive exPlanations (SHAP) analysis was applied to evaluate the contributions of age and RDW levels to mortality in this patient population. …”
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Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

“…In the neural network models, option prices were predicted using Python and its machine learning algorithms. …”
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Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system by Junxiao Shen, Luyuan Lu, Zujie Chen, Wei Guo, Shuwen Wang, Ziqiao Liu, Xuke Gong, Yiming Qi, Ruyi Jin, Cheng Zhang

“…The random forest (RF) algorithm proved to be the most effective classifier for NEPC, leading to the establishment of the NEP100 model, which demonstrated robust validation across various datasets. …”
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人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceut... by 勾岚，姜明慧，姜勇，廖晓凌，李昊，张杰，程丝 (GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si)

“…By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. However, critical challenges, such as insufficient multi-omics resources, difficulties in multi modal data integration, and limited interpretability of algorithms, remain major bottlenecks in clinical translation. …”
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Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy by Yifan Zheng, Yifan Zheng, Li Li, Wenqian Cai, Lin Li, Rongxin Zhang, Wenbin Huang, Wenbin Huang, Yulun Cao

“…To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). …”
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Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models by Wei Chen, Haotian Zheng, Binglin Ye, Tiefeng Guo, Yude Xu, Zhibin Fu, Xing Ji, Xiping Chai, Shenghua Li, Qiang Deng

“…Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. …”
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The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

“…Objective: In this research, our goal is to develop a novel framework to mitigate prediction biases in LA caused by sample limitations and data heterogeneity. …”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…In spinal deformities, models such as support vector machines and convolutional neural networks achieved over 90% accuracy in classification and curve prediction. For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. …”
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Mutational landscape and DNA methylation-based classification of squamous cell carcinoma and urothelial carcinoma by Min Ren, Midie Xu, Chen Chen, Ran Wei, Qianlan Yao, Liqing Jia, Peng Qi, Qifeng Wang, Qianming Bai, Xiaoli Zhu, Sheng Wu, Qinghua Xu, Xiaoyan Zhou

“…On the basis of public datasets and analyses via various machine learning algorithms, a DNA methylation-based classification containing 106 features by the CatBoost algorithm was constructed and reached an accuracy of 98.79% (490/496) in the training set from PanCanAtlas datasets. …”
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An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

“…A diverse set of models—ranging from traditional algorithms such as Decision Trees and Support Vector Machines to advanced ensemble methods like XGBoost and LightGBM—were developed and evaluated. …”
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Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography by Yujie Ma, Maged Ali Al-Aroomi, Yutian Zheng, Wenjie Ren, Peixuan Liu, Qing Wu, Ye Liang, Canhua Jiang

“…Abstract Objectives Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms for detecting and diagnosing dental caries using cone-beam computed tomography (CBCT) with the Mask R-CNN architecture while comparing various hyperparameters to enhance detection. …”
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Improving maize water stress diagnosis accuracy by integrating multimodal UAVs data and leaf area index inversion model by Qi Liu, Xiaolong Hu, Yiqiang Zhang, Liangsheng Shi, Wei Yang, Yixuan Yang, Ruxin Zhang, Dongliang Zhang, Ze Miao, Yifan Wang, Zhongyi Qu

“…Although models built using the random forest regression (RFR) algorithm and the combination of MIs+TIs+LAI performed best (R2 ≥ 0.575, RMSE ≤ 0.073, and RRMSE ≤ 0.18) across growth stages, their predictive advantages for PMC and NGS varied with the growth stage: PMC predictions were more accurate during stages V9 and R3, whereas NGS predictions were more accurate during stages VT and R1. …”
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Development and Validation of Early Alert Model for Diabetes Mellitus–Tuberculosis Comorbidity by Zhaoyang Ye, Guangliang Bai, Ling Yang, Li Zhuang, Linsheng Li, Yufeng Li, Ruizi Ni, Yajing An, Liang Wang, Wenping Gong

“…However, early risk prediction methods for DM patients complicated with TB (DM–TB) are lacking. …”
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Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait loci, a... by Guoqing Liu, Xiangwen Lv, Jiahui Qin, Xingqing Long, Miaomiao Zhu, Chuwen Fu, Jian Xie, Peichun He

“…Hub genes were screened using the least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms. A cellular model of AMI was established using oxygen- and glucose-deprived AC16 cells. …”
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Understanding the environmental health implications of tourism on carbon emissions in China by Jinhua Shao, Sheng Fang, Meiling Zhao, Wanxin Qian, Cai Wang

“…Studying the impact of China’s tourism industry on carbon emissions is of great significance in scientifically formulating emission reduction policies and helping China to realize its carbon reduction goals. …”
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Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method by Yue Zhang, Yuan Li, Cong Zhou, Junhui Zhou, Tiegui Nan, Jian Yang, Luqi Huang

“…The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. …”
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External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients. by Lukas Higi, Angela Lisibach, Patrick E Beeler, Monika Lutters, Anne-Laure Blanc, Andrea M Burden, Dominik Stämpfli

“…Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. …”
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity. by Ali Akbar Moosavi, Mohammad Amin Nematollahi, Mohammad Omidifard

“…Since the associated laboratory/field experiments are time-consuming and labor-intensive, pedotransfer functions (PTFs) that rely on statistical predictors are usually integrated with the existing measurements to predict Kfs in other areas of the field. In this study some of the most appropriate machine learning approaches, including variants of artificial neural networks (ANNs) were used for predicting Kfs by some easily measurable soil attributes. …”
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Identification and validation of hub m7G-related genes and infiltrating immune cells in osteoarthritis based on integrated computational and bioinformatics analysis by Zhenhui Huo, Chongyi Fan, Kehan Li, Chenyue Xu, Yingzhen Niu, Fei Wang

“…Functional enrichment, drug target prediction, and target gene-related miRNA prediction were performed for these genes. …”
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Optimization Model for Safeguarding Vulnerable Components in Integrated Energy Systems Based on Weighted Betweenness by ZHANG Chenwei, WANG Ying, LI Yaping, ZHANG Kaifeng

“…Then, the lower-level model is solved using a local linearization technique, and a “genetic-mixed integer linear programming” algorithm is proposed for solving the model with high precision and efficiency. …”
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