Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis by Peize Yu, Peize Yu, Qikai Zhong, Qikai Zhong, Xinlei Wang, Xinlei Wang, Yifang Liu, Qiang Liu, Qiang Liu, Yuqiang Zhang, Jiawei Lu, Yang Dong, Cong-hui Han, Cong-hui Han

“…A prognostic risk model was developed via multivariate Cox regression. …”
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Kolmogorov GAM Networks Are All You Need! by Sarah Polson, Vadim Sokolov

“…They provide an alternative to Transformer architectures. They are the machine learning version of Kolmogorov’s superposition theorem (KST), which provides an efficient representation of multivariate functions. …”
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Military Training Aircraft Structural Health Monitoring Leveraging an Innovative Biologically Inspired Feedback Mechanism for Neural Networks by Tarek Berghout

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Predicting cyberbullying victimisation in emerging markets and developing countries using the Global School-Based Health Survey by Paulo Ricardo Vieira Braga, Katie Rose Tyrrell

“…Subsequently, machine learning techniques were used to develop predictive models. …”
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Toward Spatio‐Temporally Consistent Multi‐Site Fire Danger Downscaling With Explainable Deep Learning by Óscar Mirones, Jorge Baño‐Medina, Swen Brands, Joaquín Bedia

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Evaluation of the impact of body mass index on venous thromboembolism risk factors. by Fatemeh Tajik, Mingzheng Wang, Xiaohui Zhang, Jie Han

“…In this paper, we investigate the interaction impacts of body mass index (BMI) on the other important risk factors for venous thromboembolism (VTE), using deep venous thrombosis (DVT) patient data from the International Warfarin Pharmacogenetics Consortium (IWPC). We apply eight machine learning techniques, including naive Bayes classifier (NB), support vector machine (SVM), elastic net regression (ENET), logistic regression (LR), lasso regression (LAR), multivariate adaptive regression splines (MARS), boosted regression tree (BRT) and random forest model (RF). …”
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Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery. by Nils Hinrichs, Tobias Roeschl, Pia Lanmueller, Felix Balzer, Carsten Eickhoff, Benjamin O'Brien, Volkmar Falk, Alexander Meyer

“…The GRU was the predominant method in this study. Uni- and multivariate neural network models proved to be superior to univariate statistical models across vital parameters and forecast horizons, and their advantage steadily became more pronounced for increasing forecast horizons. …”
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Anomaly-aware summary statistic from data batches by G. Grosso

“…Abstract Signal-agnostic data exploration based on machine learning could unveil very subtle statistical deviations of collider data from the expected Standard Model of particle physics. …”
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Estimating Winter Wheat Canopy Chlorophyll Content Through the Integration of Unmanned Aerial Vehicle Spectral and Textural Insights by Huiling Miao, Rui Zhang, Zhenghua Song, Qingrui Chang

“…Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. …”
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Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease by Qi Zhou, Mohammed Goryawala, Mercedes Cabrerizo, Warren Barker, Ranjan Duara, Malek Adjouadi

“…Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. …”
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Pesticide Residue Detection in Broccoli Based on Hyperspectral Technology and Convolutional Neural Network by Dan WANG, Yuqing LUAN, Zuojun TAN, Wei WEI

“…A support vector machine (SVM) recognition model was established for pesticide residue discrimination. …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…ObjectiveThis study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. …”
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Prediction of pathological complete response to neoadjuvant chemoimmunotherapy in non–small cell lung cancer using 18F-FDG PET radiomics features of primary tumour and lymph nodes... by Xingbiao Liu, Zhilin Ji, Libo Zhang, Linlin Li, Wengui Xu, Qian Su

“…The clinical features were screened using univariate and multivariate analyses. Machine learning models were developed using the random forest method, leading to the establishment of one clinical feature model, one primary tumour radiomics model, and two fusion radiomics models. …”
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Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram by Dabin Ren, Liqiu Liu, Aiyun Sun, Yuguo Wei, Tingfan Wu, Yongtao Wang, Xiaxia He, Zishan Liu, Jie Zhu, Guoyu Wang

“…Radiomics features were extracted from the contrast-enhanced CT images, and logistic regression analysis was employed to establish a machine learning model, including an unenhanced model (model U), an arterial phase model (model A), a venous phase model (model V), and a combined radiomics model (model U+A+V). …”
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Clinical significance of elevated tumor markers in patients with biliary pancreatitis by He Han, Zhiyuan Li, Yunfan Li, Liwen Zhang, Jixiang Chen, Xin Fan

“…We propose a clinical prediction model based on machine learning to screen variables and guide treatment adjustments for MAP.…”
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Exact Expressions for Kullback–Leibler Divergence for Univariate Distributions by Victor Nawa, Saralees Nadarajah

“…This concept is crucial in various fields, including information theory, statistics, and machine learning, as it helps in understanding how well a model represents the underlying data. …”
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Continuous glucose data construction and risk assessment application of diabetic retinopathy complications for patients with type 2 diabetes mellitus by Yaguang Zhang, Liansheng Liu, Hong Qiao

“…Compared with the three commonly used LSTM, GPR, and support vector machine, the proposed model can construct accurate results. …”
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A multimodal nomogram for benign-malignant discrimination of lung-RADS ≥4A nodules: integration of oxygen enhanced zero echo time MRI, CT radiomics, and clinical factors by Tiancai Yan, Ling Liu, Yuxin Li, Chunhui Qin, Haonan Guan, Tong Zhang

“…Univariate and multivariate logistic regression identified independent predictors, which were incorporated into a final nomogram to visualize clinical-radiomic prediction.ResultsMRI model had a similar diagnostic performance to CT model (MRI vs. …”
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Research on Partial Least Squares Method Based on Deep Confidence Network in Traditional Chinese Medicine by Wang-ping Xiong, Tian-ci Li, Qing-xia Zeng, Jian-qiang Du, Bin Nie, Chih-Cheng Chen, Xian Zhou

“…Partial least squares method has many advantages in multivariate linear regression modeling, but its internal cross-checking method will lead to a sharp reduction of the principal component, thereby reducing the accuracy of the regression equation, and the selection of principal components about the traditional Chinese medicine data is particularly sensitive. …”
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Prognostic value of circadian rhythm-associated genes in breast cancer by Ling Wang, Xiang Gao, Ximeng Zuo, Tangshun Wang, Xiaoguang Shi

“…This study aimed to identify key circadian rhythm-related genes (CRGs) using bioinformatics and machine learning, and construct a prognostic model to predict clinical outcomes. …”
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