A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain at... by Magboul M. Sulieman, Fuat Kaya, Abdullah S. Al-Farraj, Eric C. Brevik

“…The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. …”
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Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis by Zikun Huang, Qing Luo, Cuifen Xiong, Haiyan Zhu, Chao Yu, Jianqing Xu, Yiping Peng, Junming Li, Aiping Le

“…By utilizing cross-validation with a random forest algorithm approach, the training cohort achieved a sensitivity of 100% and specificity of 100%. …”
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Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning... by Xiaobo Xu, Zhaofeng Wang, Erjie Lu, Tao Lin, Hengchao Du, Zhongfei Li, Jiahong Ma

“…This study was based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Extremely Randomized Trees (ERT) models were constructed to classify carbapenem-resistant Escherichia coli (CREC) and carbapenem-resistant Klebsiella pneumoniae (CRKP). …”
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Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach by Tianshu Gu, Wensen Pan, Jing Yu, Guang Ji, Xia Meng, Yongjun Wang, Minghui Li

“…Results Decision Tree (DT) and Random Forest (RF) models consistently showed improvements in accuracy, precision, and ROC-AUC scores for Non-Hispanic Black, Hispanic/Latino, and Asian populations. …”
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Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery by Xiaodi Xu, Ya Zhang, Peng Fu, Chaoya Dang, Bowen Cai, Qingwei Zhuang, Zhenfeng Shao, Deren Li, Qing Ding

“…To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. …”
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Effects of chronic diseases on health related quality of life is mediated by sleep difficulty in middle aged and older adults by Yaoyao Wu, Zesheng Chen, Zongxue Cheng, Zhecong Yu, Kang Qin, Caixia Jiang, Jue Xu

“…We used a multi-stage cluster random sampling method to recruit participants from seven districts in Hangzhou. …”
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Soil organic carbon contents and their major influencing factors in mangrove tidal flats: a comparison between estuarine and non-estuarine areas by Ting Wu, Jia Guo, Gang Li, Yu Jin, Wei Zhao, Guangxuan Lin, Fang-Li Luo, Yaojun Zhu, Yifei Jia, Li Wen

“…We compared the SOC and soil physicochemical properties between estuarine and non-estuarine mangrove tidal flats. The Random Forest algorithm was employed to identify the main influencing factors affecting SOC. …”
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Colostrum feeding practice and associated factors among mothers who come for postnatal care to Asella referral and teaching hospital, Arsi Zone, South-East Ethiopia by Gisha Haji, Gebi Agero, Amde Eshete, Abdurahman Hasan, Ahmed Hiko

“…Even though breastfeeding is common in Ethiopia, the widespread belief that colostrum feeding causes morbidity and mortality among neonates still exists.ObjectiveTo assess the colostrum feeding practice and It’s associated factors among mothers who come for Post-natal care at Asella Referral and Teaching Hospital, Asella Town, Southeast Ethiopia from November 20, 2023, to January 25, 2024.MethodThis study employed an institutional-based cross-sectional study among 301 surveyed postnatal care mothers from November 20, 2023, to January 25, 2024. Systematic random sampling was employed to select study participants. …”
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The capacity and training needs of primary health care workers in Nyeri and Nakuru counties of Kenya: a cross-sectional study by Josphat Martin Muchangi, Kioko Kithuki, Sarah Jebet Kosgei, Mary Mathenge, Deborah Kioko, Bryson Sifuma, Billian Sawenja, Samuel Kamau

“…Multistage sampling was employed to select sub-counties in the first stage and health facilities by level within each sub-county in the second level. Systematic random sampling was then employed to select HCWs from each level of facility. …”
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Hispanic Patients Undergoing Spinal Fusion for Neuromuscular Scoliosis Sustain Health Inequalities by Armando S. Martinez, David Momtaz, Travis Kotzur, Alexandra McLennan, Abdullah Ghali, Farhan Ahmad, Ebubechi Adindu, Alan C. Santiago-Rodriquez, Ali Seifi, Brian Smith

“…Methods: A retrospective analysis was conducted using Healthcare Cost and Utilization Project data from 2016 to 2020, encompassing a random sample of 20% of procedures in the United States. …”
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Parental Technoference and Child Problematic Media Use: Meta-Analysis by Jinghui Zhang, Qing Zhang, Bowen Xiao, Yuxuan Cao, Yu Chen, Yan Li

“…The risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. A random-effects meta-analysis was performed using R (version 4.2.1; R Foundation for Statistical Computing) with the meta and metafor packages to evaluate the association and conduct moderator analyses. …”
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What are parents' preferences for Human Papillomavirus vaccination promotion messages and communication? Application of a discrete choice experiment to a French Caribbean setting by Frédérique G. E. Dorleans, Jonathan Sicsic, Valérie Henry, Isabelle Bonmarin, Gwladys Nadia Gbaguidi, Lucie Leon, Jocelyn Raude, Jacques Rosine, Judith E. Mueller

“…Scenarios differed by five attributes (diseases characteristics, vaccine safety, health professionals or institutions promoting vaccination, social conformity and optimal vaccination age). We used random effect logit and linear regression models to estimate the effects of attribute levels on vaccine acceptance and vaccine eagerness. …”
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Sexually transmitted infections and associated factors among Wollo University students, Northeast Ethiopia by Hilina Ayalew, Gedefaw Abeje, Elsabeth Addisu, Natnael Kebede, Ayalew Hassen Mohammed

“…Sample size included 598 participants. A simple random sampling technique was employed, and data were collected by pre-tested self-administered questionnaire. …”
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Machine learning analysis of emerging risk factors for early-onset hypertension in the Tlalpan 2020 cohort by Mireya Martínez-García, Guadalupe O. Gutiérrez-Esparza, Guadalupe O. Gutiérrez-Esparza, Manlio F. Márquez, Manlio F. Márquez, Luis M. Amezcua-Guerra, Enrique Hernández-Lemus, Enrique Hernández-Lemus

“…Extreme Gradient Boosting (XGBoost), Logistic Regression (LG) and Support Vector Machines (SVM) were employed to evaluate the relationship between these factors and hypertension risk.ResultsThe Random Forest (RF) Importance Percent was calculated to assess the structural relevance of each variable in the model, while Shapley Additive Explanations (SHAP) analysis quantified both the average impact and direction of each feature on individual predictions. …”
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Long-Term Outcomes of Mechanical Thrombectomy for Stroke: A Meta-Analysis by David J. McCarthy, Anthony Diaz, Dallas L. Sheinberg, Brian Snelling, Evan M. Luther, Stephanie H. Chen, Dileep R. Yavagal, Eric C. Peterson, Robert M. Starke

“…Therefore, a pooled meta-analysis using data from randomized clinical trials (RCT) was performed to assess for long-term clinical outcomes. …”
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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. …”
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Simulated low-dose dark-field radiography for detection of COVID-19 pneumonia. by Rafael C Schick, Henriette Bast, Manuela Frank, Theresa Urban, Thomas Koehler, Florian T Gassert, Andreas P Sauter, Bernhard Renger, Alexander A Fingerle, Alexandra Karrer, Marcus R Makowski, Daniela Pfeiffer, Franz Pfeiffer

“…Four experienced radiologists served as blinded readers assessing both image modalities, displayed side by side in random order. The presence of COVID-19-associated lung changes was rated on a scale from 1 to 6. …”
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Prevalence and predictors of short stature in children aged 3–18 years in Hainan Province, China: a cross-sectional study by Mi Yan, Mi Yan, Yingying Qin, Yingying Qin, Hongai Li, Chuican Huang, Haidan Li, Li Liu, Yuhua Cai, Lichun Fan, Wei Xiang, Xiaoyan Huang, Xiaoyan Huang

“…The present study was conducted to investigate the associated factors with short-stature in children.MethodsThis cross-sectional study was conducted using a staged, cluster random sampling method. A total of 26,189 children aged 3–18 years, originating from 18 cities and counties in Hainan Province, to determine the prevalence of short stature. …”
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Antihypertensive strategies for the prevention of secondary stroke: a systematic review and meta-analysis by ChunQi Wang, Ling Feng, ShuangYan Tu, Dan Wei, Rui Wang, ZhiQiang Deng, YiPing Luo

“…Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated using random or fixed-effect models in Stata 15.0. Results The analysis included 19 RCTs encompassing 72,048 patients. …”
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An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers by Bogong Liu, Huichao Liu, Junhao Tu, Jian Xiao, Jie Yang, Xi He, Haihan Zhang

“…In this study, seven different ML methods—support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), kernel ridge regression (KRR) and multilayer perceptron (MLP) were employed to predict the genomic breeding values of laying traits, growth and carcass traits in a yellow-feathered broiler breeding population. …”
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