Association between exposure to blood heavy metal mixtures and overactive bladder risk among U.S. adults: a cross-sectional study by Yanlin Zhu, Yameng Wu, Yang Wang, Yang Wang, Hua Yang, Meisheng Zhang, Hengxing Zhu, Xiaoke Chen

“…The effects of single metals on OAB risk were explored using multivariate logistic regression. Additionally, we used weighted quantile sum (WQS), quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR) models to explore the combined effect of metal mixtures on OAB risk. …”
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Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters by Fan Zhang, Fei Qi, Mengyan Sun, Peng Jiang, Minghang Zhang, Xiaomi Li, Yujie Dong, Juan Du, Liang Li, Tongmei Zhang

“…Baseline information, clinicopathological features, imaging manifestations, and blood testing results were collected and analyzed. Five machine learning methods, including logistic regression (LR), random forest (RF), support vector machine (SVM), decision tree (DT), and neural network (NN), were employed to develop a screening model for PTB-LC. …”
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A quantitative geospatial analysis of the risk that Boko Haram will target a school. by Lirika Sola, Youdinghuan Chen, V S Subrahmanian

“…Third, we train several predictive machine learning models and assess their predictive efficacy. …”
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Increased risk of COVID-19-related admissions in patients with active solid organ cancer in the West Midlands region of the UK: a retrospective cohort study by Peter Nightingale, Fahad Mahmood, Akinfemi Akingboye, Nabeel Amiruddin, Olorunseun O Ogunwobi

“…The MLA model examined the contribution of predictive variables for 90-day survival (area under the curve: 0.749); with transplant patients, age, male gender and diabetes mellitus being predictors of greater mortality.Conclusions Active cancer diagnosis has a threefold increase in risk of hospitalisation with COVID-19. …”
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Ambulatory Smoking Habits Investigation based on Physiology and Context (ASSIST) using wearable sensors and mobile phones: protocol for an observational study by Donghui Zhai, Giuseppina Schiavone, Ilse Van Diest, Elske Vrieze, Walter DeRaedt, Chris Van Hoof

“…First, linear regression and linear mixed model will be used to estimate whether a factor or pattern have consistent (p value<0.05) correlation with smoking. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. Subsequently, multivariate COX regression analysis was performed to identify independent risk factors for constructing the predictive nomogram. …”
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Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies by Saleh Ghadernejad, Kamran Esmaeili, Mariano P. Consens

“…Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. …”
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Assessing and Forecasting Natural Regeneration in Mediterranean Landscapes After Wildfires by Paraskevi Oikonomou, Vassilia Karathanassi, Vassilis Andronis, Ioannis Papoutsis

“…To predict vegetation regrowth, two time series models (ARMA, VARIMA) and two machine learning-based ones (random forest, XGBoost) were tested. …”
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Propensity score methodology for confounding control in health care utilization databases by Elisabetta Patorno, Alessandra Grotta, Rino Bellocco, Sebastian Schneeweiss

“…This methodology offers researchers many advantages compared with conventional multivariate models: it directly focuses on the determinants of treatment choice, facilitating the understanding of the clinical decision-making process by the researcher; it allows for graphical comparisons of the distribution of propensity scores and truncation of subjects without overlapping PS indicating a lack of equipoise; it allows transparent assessment of the confounder balance achieved by the PS at baseline; and it offers a straightforward approach to reduce the dimensionality of sometimes large arrays of potential confounders in utilization databases, directly addressing the “curse of dimensionality” in the context of rare events. …”
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Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis by Marko Orošnjak, Felix Saretzky, Slawomir Kedziora

“…Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. …”
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QSPR analysis of physico-chemical and pharmacological properties of medications for Parkinson’s treatment utilizing neighborhood degree-based topological descriptors by Vignesh Ravi, Natarajan Chidambaram

“…The study employs linear, quadratic, cubic, and multiple linear regression models. A comparative analysis is conducted using several well-known degree-based indices alongside the selected open and closed neighborhood degree-sum-based indices within both univariate and multivariate regression methodologies.…”
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Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study by Jiaxin Tian, Qiurui Zhang, Minhua Peng, Leixin Guo, Qianqian Zhao, Wei Lin, Sitong Chen, Xuefei Liu, Simin Xie, Wenxin Wu, Yijie Li, Junqi Wang, Jin Cao, Ping Wang, Min Zhou

“…Potential VOC markers for classification were identified by univariate and multivariate analyses. Subsequently, classification models were established by machine learning algorithms, based on these VOC markers along with baseline characteristics. …”
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Marine data assimilation in the UK: the past, the present, and the vision for the future by J. Skákala, J. Skákala, D. Ford, K. Haines, K. Haines, A. Lawless, A. Lawless, M. J. Martin, P. Browne, M. Chrust, S. Ciavatta, A. Fowler, A. Fowler, D. Lea, M. Palmer, A. Rochner, A. Rochner, J. Waters, H. Zuo, D. S. Banerjee, D. S. Banerjee, M. Bell, D. M. Carneiro, Y. Chen, Y. Chen, S. Kay, S. Kay, D. Partridge, D. Partridge, M. Price, R. Renshaw, G. Shapiro, J. While

“…We also advocate for integrated approaches, such as strongly coupled DA (ocean–atmosphere, physics–biogeochemistry, and ocean–sea ice) and the use of ML/AI components (e.g. for multivariate increment balancing, bias correction, model emulation, observation re-gridding, or fusion).…”
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Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma by Sonoko Oshima, Jingwen Yao, Samuel Bobholz, Raksha Nagaraj, Catalina Raymond, Ashley Teraishi, Anna-Marie Guenther, Asher Kim, Francesco Sanvito, Nicholas S Cho, Blaine S. C. Eldred, Jennifer M Connelly, Phioanh L Nghiemphu, Albert Lai, Noriko Salamon, Timothy F Cloughesy, Peter S LaViolette, Benjamin M Ellingson

“…Aim: A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. …”
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Structured-illumination reflectance imaging for the evaluation of microorganism contamination in pork: effects of spectral and imaging features on its prediction performance by Binjing Zhou, Xiaohua Liu, Yan Ge, Kang Tu, Jing Peng, Juan Francisco García-Martín, Jie Wu, Weijie Lan, Leiqing Pan

“…Besides, the prediction models based on the amplitude component or direct component image textural features and the data fusion models using spectrum and textural features from direct component and amplitude component images cannot significantly improve their prediction accuracy. …”
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Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage by Lingxu Chen, Xiaochen Wang, Sihui Wang, Xuening Zhao, Ying Yan, Mengyuan Yuan, Shengjun Sun

“…Eight machine learning algorithms were applied to construct radiomics-only and radiomics-clinical fusion nomogram models. …”
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Generalizing the Cross Product to <i>N</i> Dimensions: A Novel Approach for Multidimensional Analysis and Applications by Samir Brahim Belhaouari, Yunis Carreon Kahalan, Ilyasse Aksikas, Abdelouahed Hamdi, Ismael Belhaouari, Elias Nabel Haoudi, Halima Bensmail

“…This generalization not only enriches the theoretical foundation of vector calculus but also opens up new applications in high-dimensional data analysis, machine learning, and multivariate time series. The results suggest that this extension of the cross product could serve as a powerful tool for modeling complex interactions in multi-dimensional spaces, with potential implications across various scientific and engineering disciplines.…”
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Development and validation of a nomogram for differentiating immune checkpoint inhibitor-related pneumonitis from pneumonia in patients undergoing immunochemotherapy: a multicenter... by Linli Duan, Guanglu Liu, Zijie Huang, Rong Chen, Di Mo, Yuxiao Xia, Jiazhu Hu, Mengzhang He

“…Utilizing the random forest machine learning method, optimal development and validation cohort allocation ratios (in a ratio of 8:2) were determined for the predictive model. …”
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Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices by Hikaru Sugimoto, Ken-ichi Hironaka, Tomoaki Nakamura, Tomoko Yamada, Hiroshi Miura, Natsu Otowa-Suematsu, Masashi Fujii, Yushi Hirota, Kazuhiko Sakaguchi, Wataru Ogawa, Shinya Kuroda

“…Multivariate and machine learning models indicate AC_Var’s contribution to predicting clamp-derived DI independent from other CGM-derived indices. …”
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Association between triglyceride-glucose index and carotid atherosclerosis in Chinese steelworkers: a cross-sectional study by Haoyue Cao, Qinglin Li, Juxiang Yuan

“…The association between the TyG index and CAS was analyzed using multivariable logistic regression and restricted cubic spline (RCS) models. …”
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