Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics by YANG Taotao, WANG Xianqi, CHEN Cancan

“…The radiomic and clinical features were subsequently combined to develop a comprehensive model. All the 3 classification models were built using random forest (RF) machine learning. …”
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Normal-Weight Abdominal Obesity: A Risk Factor for Hypertension and Cardiometabolic Dysregulation by Jinyu Sun, Qiang Qu, Yue Yuan, Guozhen Sun, Xiangqing Kong, Wei Sun, Tianyu Xu, Xiaoxia Fu

“…The demographic characteristics and cardiometabolic risk factors across waist circumference quartiles were summarized. We used adjusted multivariate logistic regression models, subgroup analysis, and restricted cubic spline to analyze the association between waist circumference and the prevalence of hypertension. …”
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Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations by Y. Qi, E. Mandanici, M. Helmy, F. Trevisiol, G. Bitelli

“…Overall, the Transformer model demonstrated exceptional ability in capturing spatial-temporal dependencies in multivariate time-series data. …”
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Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning by LI Xin, LI Shuhua, CHEN Hao, SI Lei, WEI Dong, ZOU Xiaoyu

“…A random hypergraph convolution mechanism was introduced into the feature nodes of the ICBL model to fully exploit the complex multivariate structural information in the vibration data of the shearer cutting unit gearbox, thereby enhancing the representation of fault features. …”
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Development of a personalized digital biomarker of vaccine-associated reactogenicity using wearable sensors and digital twin technology by Steven R. Steinhubl, Jadranka Sekaric, Maged Gendy, Huaijian Guo, Matthew P. Ward, Craig J. Goergen, Jennifer L. Anderson, Sarwat Amin, Damen Wilson, Eustache Paramithiotis, Stephan Wegerich

“…Methods We use a wearable torso sensor patch and a machine learning method of similarity-based modeling (SBM) to create a physiologic digital twin for 88 people receiving 104 COVID vaccine doses. …”
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Indicators of insulin resistance as predictors of 28-day mortality in patients with VA-ECMO: a retrospective study by You Zhou, Zhi Cheng, Pingping Gu, Yu Zhang, Wanying Xu, Xin Wang

“…To evaluate the associations of insulin resistance indicators such as triglyceride-glucose (TyG), metabolic score for insulin resistance (METS-IR), triglyceride-to-high-density-lipoprotein cholesterol ratio (TG/HDL-C), and triglyceride glucose-body mass index (TyG-BMI) with 28-day mortality. A multi-stage modeling strategy was adopted. Firstly, risk factors were screened through univariate and multivariate Cox regression; Further combine Least Absolute Shrinkage and Selection Operator (LASSO) regression (L1 regularization), random forest and gradient boosting machine (GBM) for multi-method feature screening, and use ridge regression (L2 regularization) to control collinearity to construct a joint prediction model; Finally, the model efficacy was verified through C-index, time-dependent receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement index (NRI), and comprehensive discriminant improvement index (IDI).ResultsTyG, METS-IR, TG/HDL-C, and TyG-BMI independently predicted an increased risk of death (all p < 0.01). …”
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Interrelationships Between Inflammatory Score, Delayed Cerebral Ischemia and Unfavorable Outcome in Patients with aSAH: A Four-Way Decomposition by Zhang P, Zhu H, Li X, Qian Y, Zhu Y, Zhang W, Yan Z, Ni H, Lin Z, Lin X, Li Z, Zhuge Q, Zeng B

“…Multivariate logistic regression was performed to identify the association of inflammatory score with DCI and poor outcome. …”
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Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer by Yun Zhu, Shuni Zhang, Wei Wei, Li Yang, Lingling Wang, Ying Wang, Ye Fan, Haitao Sun, Zongyu Xie

“…Independent risk factors for clinical-radiological features were obtained by univariate and multivariate logistic regression analysis, and clinical model (CM) was constructed. …”
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The influence of hydroelectric power generation and water level variability on market clearing prices: A case study of Türkiye by Tamer Emre

“…To explore this, the study applies a comprehensive methodological framework combining multivariate regression, correlation analysis, impulse response functions (IRF), Granger causality tests, and machine learning models—specifically, Bidirectional LSTM and Stacked LSTM architectures.Findings reveal a weak correlation (R2 = 0.065) between hydroelectric generation and MCP, with no statistically significant causal relationship. …”
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Tumor associated neutrophils promote prostate cancer progression by mediating neutrophil trap secretion through PSMA1- NF-κB-HIF-1α signaling axis by Qian Dai, Qian Dai, Hua Wang, Hua Wang, Fang Li, Fang Li, Runchun Huang, Runchun Huang, Chenjun Jiang, Chenjun Jiang, Liuya Yuan, Liuya Yuan, Yayun Wang, Yayun Wang, Xun Li, Xun Li

“…Here, ① in this retrospective analysis of clinical data of PCa patients, we discovered that patients with PCa have elevated neutrophil levels and a greater risk of complications than patients with prostatic hyperplasia. ② We integrated LASSO regression analysis and machine learning analyses to create a prognostic prediction model involving 6 genes, and this model effectively categorized patients into high-risk and low-risk groups, with higher risk scores indicating a poorer prognosis. …”
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An Integrated Platform Combining Immersive Virtual Reality and Physiological Sensors for Systematic and Individualized Assessment of Stress Response (bWell): Design and Implementat... by Budhachandra Khundrakpam, Melanie Segado, Jesse Pazdera, Vincent Gagnon Shaigetz, Joshua A Granek, Nusrat Choudhury

“…ConclusionsThe study presents a novel VR-based experimental setup that allows a systematic and individualized assessment of stress responses, paving the way for future research to identify features that confer stress resilience and help individuals manage stress effectively. While our conceptual model highlights the role of HRV in providing valuable insights into stress responses, future research will involve multivariate and machine learning analyses to predict individual stress responses based on comprehensive sensor data, including EMG and the VR-based behavioral data, ultimately guiding personalized stress management interventions.…”
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Joint and independent associations of dietary vitamin intake and prevalence of cardiovascular disease in chronic kidney disease subjects: a cross-sectional analysis by Guoqing Wang, Guoqing Wang, Luojun Huang, Luojun Huang, Wenwen Yue, Wenwen Yue, Jun Feng, Jun Feng

“…We performed weighted multivariate logistic regression models to analyze the association of single dietary vitamins intake with CVD. …”
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Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory by Xiaolu Wei, Binbin Lei, Hongbing Ouyang, Qiufeng Wu

“…This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. …”
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Environmental pollution and economic growth in the European Union countries: A systematic literature review by Suproń Błażej

“…Through a systematic review of approximately 1,250 scientific publications, machine learning techniques, and multivariate statistical analysis, significant yet complex relationships between these variables have been identified. …”
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Association of exposure to urinary and blood heavy metals with visual disability among U.S. adults in NHANES 2013–2018 by Lingyu Dai, Qian Zhou, Yu Gao, Guannan Su, Qingyan Jiang, Lan Xia, Peizeng Yang

“…The urinary barium (Ba), cadmium (Cd), cesium (Cs), cobalt (Co), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (Tl), tin (Sn), tungsten (Tu), and mercury (Hg) and blood Pb, Cd, and Hg were included for analysis. We used multivariate logistic regression, weighted quantile sum (WQS) regression, quantile-based gcomputation (qgcomp) regression, and Bayesian kernel machine regression (BKMR) to assess the mixed-metal effect on visual disability. …”
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Cardiac computer tomography-derived radiomics in assessing myocardial characteristics at the connection between the left atrial appendage and the left atrium in atrial fibrillation... by Xiao-Xuan Wei, Cai-Ying Li, Hai-Qing Yang, Peng Song, Bai-Lin Wu, Fang-Hua Zhu, Jing Hu, Xiao-Yu Xu, Xin Tian

“…The radiomics model was built by extracting radiomic features of the myocardial tissue using Pyradiomics, and employing Least absolute shrinkage and selection operator (LASSO) method for feature selection, combining random forest with support vector machine (SVM) classifier.ResultsThere were 82 cases in the AF group [44 males, 65.00 (59, 70)], and 56 cases in the control group (21 males, 61.09 ± 7.18). …”
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Exploring Postharvest Metabolic Shifts and NOX2 Inhibitory Potential in Strawberry Fruits and Leaves via Untargeted LC-MS/MS and Chemometric Analysis by Georgia Ladika, Paris Christodoulou, Eftichia Kritsi, Thalia Tsiaka, Georgios Sotiroudis, Dionisis Cavouras, Vassilia J. Sinanoglou

“…Additionally, a machine learning-based predictive model was applied to evaluate the NOX2 inhibitory potential of 24 structurally characterized metabolites. …”
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Analysis of factors associated with extended recovery time after colonoscopy. by Patrick C Eschenfeldt, Uri Kartoun, Curtis R Heberle, Chung Yin Kong, Norman S Nishioka, Kenney Ng, Sagar Kamarthi, Chin Hur

“…In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various "machine learning" techniques to see if better prediction could be achieved.…”
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Metabolic dysfunction-associated steatotic liver disease (MASLD) biomarkers and progression of lower limb arterial calcification in patients with type 2 diabetes: a prospective coh... by Damien Denimal, Maharajah Ponnaiah, Franck Phan, Anne-Caroline Jeannin, Alban Redheuil, Joe-Elie Salem, Samia Boussouar, Pauline Paulstephenraj, Suzanne Laroche, Chloé Amouyal, Agnès Hartemann, Fabienne Foufelle, Olivier Bourron

“…The predictive ability of these biomarkers of MASLD on LLACS progression was assessed through univariate and multivariate linear regression models, principal component regression analysis, as well as machine learning algorithms. …”
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Dissecting Trichoderma antagonism: Role of strain identity, volatiles, biomass, and morphology in suppressing cacao pathogens by Insuck Baek, Jishnu Bhatt, Jae Hee Jang, Seunghyun Lim, Amelia Lovelace, Minhyeok Cha, Dilip Lakshman, Moon S. Kim, Lyndel W. Meinhardt, Sunchung Park, Ezekiel Ahn

“…Significant morphological changes were observed in both interacting fungi. Machine learning models predicted pathogen colony size with high accuracy (test set R2 up to 0.94), identifying the specific Trichoderma-pathogen pair identity and Trichoderma circularity as the most crucial predictors. …”
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