Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients by Xiaoyan Yin, Yongbin Cui, Tonghai Liu, Zhenjiang Li, Huiling Liu, Xingmin Ma, Xue Sha, Changsheng Ma, Dali Han, Yong Yin

“…Abstract Purpose This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC) patients. …”
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The relationship between mixed exposure to blood metal and serum neurofilament light chain levels in the general U.S. population: an unsupervised clustering approach by Jiyu Nie, Jiyu Nie, Lin Wen, Zhentian Lai, Zhentian Lai, Chuhang Lin, Chuhang Lin, Haiyin Li, Haiyin Li, Jing Zhang, Jing Zhang, Shen Xie, Shen Xie, Xiaosong Ben, Chunxia Jing, Chunxia Jing

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Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders by April Yujie Yan, Traci Jenelle Speed, Casey Overby Taylor

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Simultaneous determination of 7 thiols associated proteins in lymphoma patients’serum and cerebrospinal fluid by UHPLC-HRMS technique by Qingkun Ma, Mei Zhang, Yongshuai Fan, Han Zhao, Kun Wang, Xiaojing Wang, Yanling Mu

“…Furthermore, a novel PCNSL monitoring model was developed based on different those combined with machine learning algorithm. …”
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Association between waist circumference and fatty liver disease in older adult population: a cross-sectional study in Urumqi by Mingdong Zhang, E. Zhao, Gaofeng Sun

“…Variables were further screened using machine learning models such as random forest classifier and Lasso. …”
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Efficiency of prognostic scores in predicting the new-onset atrial fibrillation in patients with ST-elevation myocardial infarction after percutaneous coronary intervention by R. L. Pak, B. I. Geltser, K. I. Shahgeldyan, N. S. Kuksin, E. A. Kokarev, V. N. Kotelnikov

“…To compare the effectiveness of the POAF, PAFAC, COM-AF, HATCH, ms2HEST and CHA2DS2-VASc scores for predicting new-onset atrial fibrillation (AF) in patients with ST-elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI), as well as to develop novel prognostic models based on machine learning methods.Material and methods. …”
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Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents by Muhammad Mahbubur Rahman, Muhammad Mahbubur Rahman

“…The multivariable logistic regression models identified specific Fitbit measurements that significantly predicted ADHD diagnosis. …”
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Health effects of mixed metal exposure on accelerating aging among the elderly population by Chuanli Yang, Jijun Zhang, Haohan Liu, Qin Hong, Yunhe Fan, Jie An, Haijia Zhang, Xiaobing Shen, Xiushan Dong

“…GrimAge acceleration (AgeAccelGrim) was calculated as the residuals from regressing DNA methylation GrimAge on chronological age. Weighted multivariable logistic regression models were applied to analyze the relationship between metal exposure with AgeAccelGrim. …”
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Classifying sex with volume-matched brain MRI by Matthis Ebel, Martin Domin, Nicola Neumann, Carsten Oliver Schmidt, Martin Lotze, Mario Stanke

“…On the other hand, multivariate statistical or machine learning methods that analyze MR images of the whole brain have reported respectable accuracies for the task of distinguishing brains of males from brains of females. …”
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Identification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics by Weisheng Zhan, Yixin Li, Hui Luo, Jiang He, Jiao Long, Yang Xu, Ying Yang

“…Multivariate logistic regression analysis was used to identify the most relevant clinical features, which were then combined with radiomic features to create clinical and integrated models. …”
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Forecasting Achievement of Inactive Disease in Juvenile Idiopathic Arthritis with Artificial Intelligence by Ana I. Rebollo-Giménez, Francesca Ridella, Silvia Maria Orsi, Elena Aldera, Marco Burrone, Valentina Natoli, Silvia Rosina, Alessandro Consolaro, Esperanza Naredo, Angelo Ravelli, Davide Cangelosi

“…Multivariate time series forecasting, coupled with the Random Forest method, was used to train the machine learning (ML) forecasting model. …”
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Establishing a Prognostic Model Correlates to Inflammatory Response Pathways for Prostate Cancer via Multiomic Analysis of Lactylation-Related Genes by Qinglong Du, CuiYu Meng, Wenchao Zhang, Li Huang, Chunlei Xue

“…Through integrative bioinformatics interrogation of lactylation-associated molecular signatures, we established prognostic correlations using multivariable feature selection methodologies. Initial screening via differential expression analysis (limma package) coupled with Cox proportional hazards modeling revealed 11 survival-favorable regulators and 16 hazard-associated elements significantly linked to biochemical recurrence. …”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. …”
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Risk prediction model of cognitive performance in older people with cardiovascular diseases: a study of the National Health and Nutrition Examination Survey database by Hui Wang, Sensen Wu, Dikang Pan, Yachan Ning, Cong Wang, Jianming Guo, Yongquan Gu

“…The study employed the Minor Absolute Shrinkage and Selection Operator (LASSO) regression model, in conjunction with multivariate logistic regression analysis, to identify relevant variables and develop a predictive model. …”
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Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study by Zhu Zhu, Kaiying Wu, Jian Lu, Sunxian Dai, Dabo Xu, Wei Fang, Yixing Yu, Wenhao Gu

“…Clinical variables were selected using univariate and multivariate analyses. Clinical, CR, DLR, CR-DLR, and clinical-radiomics (Clin-R) models were built using support vector machines. …”
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Automated identification of older adults at risk for cognitive decline by Darlene P. Floden, Olivia Hogue, Saket A. Saxena, Anita D. Misra‐Hebert, Alex Milinovich, Michael B. Rothberg, Elizabeth R. Pfoh, Robyn M. Busch, Kamini Krishnan, Robert J. Fox, Michael W. Kattan

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2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma by Xiaoxin Huang, Xiaoxiao Huang, Kui Wang, Haosheng Bai, Xiuxian Lu, Guanqiao Jin

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Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics by Lifei Hu, Yifan Wang, Xin Wu, Yuanyuan Shan, Fengxiao Zhu, Fan Zhang, Qiang Yang, Mingxing Liu

“…Spectral preprocessing methods (Savitzky-Golay smoothing, normalization, standard normal variate, and multiplicative scatter correction) enhanced machine learning performance, with support vector machine (SVM), radial basis function (RBF), and convolutional neural network (CNN) models achieving scores of 1.0000 across performance metrics, indicating strong generalization and robustness. …”
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Relevance of superoxide dismutase type 1 to lipoid pneumonia: the first retrospective case-control study by Yinan Hu, Yanhong Ren, Yinzhen Han, Zhen Li, Weiqing Meng, Yuhui Qiang, Mengyuan Liu, Huaping Dai

“…SOD1 had the highest importance score in ML-based LP predictive models. Additionally, advanced age may be associated with higher mortality in LP. …”
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Identifying trade-offs and synergies among land use functions using an XGBoost-SHAP model: A case study of Kunming, China by Kun Li, Junsan Zhao, Yongping Li, Yilin Lin

“…Then, an interpretable machine learning model (XGBoost-SHAP) was utilized to provide an intuitive explanation of the nonlinear response mechanism of LUF trade-offs/synergies. …”
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