Developing a molecular diagnostic model for heatstroke-induced coagulopathy: a proteomics and metabolomics approach by Qingbo Zeng, Qingwei Lin, Longping He, Lincui Zhong, Ye Zhou, Xingping Deng, Nianqing Zhang, Qing Song, Qing Song, Jingchun Song, Jingchun Song

“…Building on these findings, an optimal machine learning diagnostic model was developed to boost the accuracy of HSIC diagnosis, integrating LDHA, NGAL, prothrombin, and GBE as key biomarkers.…”
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PATTERN FORECASTING OF PERFORMANCE INDICES FOR AIR-AND-SCREEN CLEANER FROM CHAFFING EFFICIENCY RISE by Yury Ivanovich Yermolyev, Andrey Vladimirovich Butovchenko, Artem Alexandrovich Doroshenko

“…As a result of modeling, a trend of the index variation of the machine performance and their in terrelation is r e vealed. …”
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Construction and validation of a machine learning-based nomogram model for predicting pneumonia risk in patients with catatonia: a retrospective observational study by Yi-chao Wang, Qian He, Yue-jing Wu, Li Zhang, Sha Wu, Xiao-jia Fang, Shao-shen Jia, Fu-gang Luo

“…The best-performing model was selected for multivariable analysis to determine the variables included in the final Nomogram Model. …”
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Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review by Jing Gao, Yujun Yao, Jingdong Xue, Ruiyao Chen, XingYu Yang, Jie Xu, Weiwei Cheng

“…Abstract Objective To assess the methodological quality and the risk of bias, of studies that developed prediction models using Machine Learning (ML) techniques to estimate prenatal birthweight. …”
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Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy. by Andres Portocarrero-Bonifaz, Salman Syed, Maxwell Kassel, Grant W McKenzie, Vishwa M Shah, Bryce M Forry, Jeremy T Gaskins, Keith T Sowards, Thulasi Babitha Avula, Adrianna Masters, Jose G Schneider, Scott R Silva

“…Machine learning presents a novel solution to creating multivariable models for personalized radiation therapy.…”
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Quantitative detection of alkali metal Na based on laser-induced breakdown spectroscopy technology by Sijie SHEN, Chi LI, Wangzheng ZHOU, Zhenzhen WANG, Junjie YAN, Zongyu HOU, Zhe WANG, Yoshihiro DEGUCHI

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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

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Linear and Nonlinear Multivariate Classification of Iranian Bottled Mineral Waters According to Their Elemental Content Determined by ICP-OES by J.B. Ghasemi, E. Zolfonoun, R. Khosrokhavar

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Predictive model of malignancy probability in pulmonary nodules based on multicenter data by Yuyan Huang, Yong Chen, Fang He, Li Jiang

“…Multiple machine learning classification models were employed for analysis, with the optimal model ultimately selected. …”
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Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis by Moh. Dede, Sunardi Sunardi, Kuok-Choy Lam, Susanti Withaningsih, Hendarmawan Hendarmawan, Teguh Husodo

“…Data was acquired from Landsat-series imageries from 1993 to 2023, and LULC analyses were conducted using classification and regression trees (CART), random forest (RF), and support vector machine (SVM). We analyzed seven independent variables, including slope (X1), elevation (X2), main river (X3), population (X4), central business district (X5), distance from the past settlements (X6), and accessibility (X7) using multivariate analysis. …”
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Exploring Downscaling in High-Dimensional Lorenz Models Using the Transformer Decoder by Bo-Wen Shen

“…This study highlights several key findings and areas for future research: (1) a set of large-scale variables, analogous to multivariate analysis, which retain memory of their connections to smaller scales, can be effectively leveraged by trained empirical models to estimate irregular, chaotic small-scale variables; (2) modern machine learning techniques, such as FFNN and transformer models, are effective in capturing these downscaling processes; and (3) future research could explore both downscaling and upscaling processes within a triple-scale system (e.g., large-scale tropical waves, medium-scale hurricanes, and small-scale convection processes) to enhance the prediction of multiscale weather and climate systems.…”
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Machine learning-based exploration of the associations between multiple minerals' intake and thyroid dysfunction: data from the National Health and Nutrition Examination Survey by Shaojie Liu, Shaojie Liu, Weibin Huang, Yaming Lin, Yifei Wang, Hongjin Li, Xiaojuan Chen, Yijia Zou, Bo Chen, Baochang He, Zhiping Yang, Jing Fan

“…A total of 7,779 participants with aged over 20 years were effectively enrolled in this study and categorized into hyperthyroidism or hypothyroidism groups. Weighted multivariate logistic regression model along with three machine learning models WQS, qg-comp, and BKMR were employed to investigate the individual and joint effect of multiple minerals' consumption on TD.ResultsAmong 7,779 subjects, 134 participants were diagnosed as hyperthyroidism and 184 participants were diagnosed as hypothyroidism, with prevalence of 1.6 and 2.4%, respectively. …”
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Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study by Chu Wang, Chu Wang, JunYe Wen, ZiYi Su, HanXiang Yu

“…The above five parameters were incorporated into the Ml model. Comprehensive analysis revealed that the Light Gradient Boosting Machine (LightGBM) classification model was the optimal model, with the area under the curve (AUC) of the validation cohort was 0.876, the 95% confidence interval was 0.8139–0.938, the accuracy was 0.843, the sensitivity was 0.805, and the specificity was 0.857, with AUC of validation cohort was 0.876. …”
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Multivariate and long-term time series analysis to assess the effect of nitrogen management policy on groundwater quality in Wallonia, BE by E. Verstraeten, A. Alonso, L. Collier, M. Vanclooster

“…Future studies could explore integrating modelling approaches to supplement observational data with modelled data as inputs to statistical models or to combine data-driven models and process-based models.…”
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A novel machine learning based framework for developing composite digital biomarkers of disease progression by Song Zhai, Andy Liaw, Judong Shen, Yuting Xu, Vladimir Svetnik, James J. FitzGerald, James J. FitzGerald, Chrystalina A. Antoniades, Dan Holder, Marissa F. Dockendorf, Jie Ren, Richard Baumgartner

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Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning by Yuanyuan Wu, Cai Liu, Jinge Huang, Fang Wang

“…Multivariate Cox regression analysis was also conducted to establish the prognostic models. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…This approach adeptly handles non-linearity, covariation, and interactive effects among predictors. For modeling multivariate data sets, a hybrid strategy combining a self-organizing map (SOM) and RF is harnessed to effectively tackle the challenges. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…This approach adeptly handles non-linearity, covariation, and interactive effects among predictors. For modeling multivariate data sets, a hybrid strategy combining a self-organizing map (SOM) and RF is harnessed to effectively tackle the challenges. …”
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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study by Yuting Ge, Wenchuan Chang, Lixiao Xie, Yan Gao, Yue Xu, Huie Zhu

“…The SHapley Additive exPlanation (SHAP) method was used to interpret the results of the best‐performing model. Results One multivariate logistic regression model and seven machine learning models were constructed. …”
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Grape vine (Vitis vinifera) yield prediction using optimized weighted ensemble machine learning approach by Nobin Chandra Paul, Pratapsingh S. Khapte, Navyasree Ponnaganti, Sushil S. Changan, Sangram B. Chavan, K. Ravi Kumar, Dhananjay D. Nangare, K. Sammi Reddy

“…A diverse set of machine learning (ML) models, including Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting (XgBoost), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Cubist and Multivariate Adaptive Regression Splines (MARS), were employed to model the grapevine yield. …”
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