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  1. 36321
    “…Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. …”
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    Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java
  2. 36322
    “…Here, we address this gap by applying the principles of landscape approaches and knowledge co-production to co-produce a theory of change to address current unsustainable landscape management and associated conflicts in the Kalomo Hills Local Forest Reserve No. P.13 (KFR13) of Zambia. The participatory process engaged a diverse range of stakeholders including village head people, local and international researchers, district councillors, and civil society representatives amongst others. …”
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    Co-producing theory of change to operationalize integrated landscape approaches
  3. 36323
    “…Relative risk (RR) was calculated from forest plots and outcomes using random-effects model (REM). …”
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    Oncological outcomes of anatomic versus non-anatomic resections for small hepatocellular carcinoma: systematic review and meta-analysis of propensity-score matched studies
  4. 36324
    “…The dominant climate variables affecting NDVI variation were selected through the combination of random forest model and stepwise regression method to improve the residual trend analysis, and on this basis, twelve possible scenarios were established to evaluate the driving factors of different degraded grasslands. …”
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    Impacts of climate change and human activities on different degraded grassland based on NDVI
  5. 36325
    “…Extensive and close connections among genera and species were observed in the correlation analysis. Moreover, a random forest classifier was constructed using specific enriched species, which can distinguish the stone side from the non-stone side with an accuracy of 71.2%. …”
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    The renal pelvis urobiome in the unilateral kidney stone patients revealed by 2bRAD-M
  6. 36326
    “…Based on supervised learning algorithms, namely random forest classifier (RFC), artificial neural network(ANN), support vector machine(SVM), decision tree(DT), and extreme gradient boosting gradient(XGboost) algorithm, the LNM prediction model was constructed, and the prediction efficiency of ML-based model was evaluated via receiver operating characteristic curve(ROC) and decision curve analysis(DCA). …”
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    Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models
  7. 36327
    “…METHODS: The IBM® MarketScan® Commercial Subset (10/01/2015–12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non–trauma-based features. …”
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    Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach
  8. 36328
    “…MVA-BN-Filo is a multivalent vector encoding EBOV, SUDV, and MARV GPs, and Taï Forest nucleoprotein. This Phase 1, randomized, double-blind, placebo-controlled study enrolled healthy adults (18–50 years) into four groups, randomized 5:1 (active:placebo), to assess different Ad26.Filo and MVA-BN-Filo vaccine directionality and administration intervals. …”
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    First-in-human study to evaluate safety, tolerability, and immunogenicity of heterologous regimens using the multivalent filovirus vaccines Ad26.Filo and MVA-BN-Filo administered in different sequences and schedules: A randomized, controlled study
  9. 36329
    “…Seven thousand, six hundred thirty-nine had no prior history of AF and were eligible to train and validate machine learning algorithms. Random survival forests (RSFs) were used to predict new-onset AF and compared to Cox proportional-hazard (CPH) models. …”
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    Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information
  10. 36330
    “…Suggestions to improve the educational intervention were to provide more details about the forest plot, add more digital content or images, provide more details about the methodological steps of an SR, add descriptions about practical applications of SRs and provide links to additional educational materials. …”
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    Creating an online educational intervention to improve knowledge about systematic reviews among healthcare workers: mixed-methods pilot study
  11. 36331
    “…An observational, retrospective, cross-sectional cohort study was conducted in accordance with TRIPOD statement. Breiman's random forest model was applied to calculate variable importance (VIMP) for items in PG-SGA and EORTC QLQ-C30 (Chinese version) for nutritional recommendation. …”
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    Prognosis-Related Nutritional Score for Cancer Patients (PRNS): a clinical nutritional score derived from a retrospective cohort study
  12. 36332
    “…A feature selection algorithm was used to select six important features for D. Using a random forest classifier, these features were capable of classifying D(+) and D(−) with an accuracy of 82.5%. …”
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    Identification of repurposed drugs targeting significant long non-coding RNAs in the cross-talk between diabetes mellitus and Alzheimer’s disease
  13. 36333
    “…In this study, data recorded during the initial ten days of monitoring were retrospectively examined, and a random forest model was developed to predict SpO(2) < 94 % on a given day using SpO(2) and HR data from the two previous days and day of discharge. …”
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    Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge
  14. 36334
    “…The targeted genes/non-coding RNAs were further reduced using random forest and forward feature selection (ffs) models. …”
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    Identification of risk variants related to malignant tumors in children with birth defects by whole genome sequencing
  15. 36335
    “…METHODS: We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March–November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). …”
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    Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission
  16. 36336
    “…The application interface has an intuitive design set out in four main menus: file upload; graphical description (forest and ROC plane plots); meta-analysis (pooling of sensitivity and specificity, estimation of likelihood ratios and diagnostic odds ratio, sROC curve); and summary of findings (impact of test through downstream consequences in a hypothetical population with a given prevalence). …”
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    Meta-DiSc 2.0: a web application for meta-analysis of diagnostic test accuracy data
  17. 36337
    “…Following multiple imputation of variables with more than 5% missing values, random forest analysis was applied to the imputed data. Right ventricular (RV) basal diameter (RVD1), RV mid-cavity diameter (RVD2), tricuspid annular plane systolic excursion, RV systolic pressure, hypertension, RV dysfunction, troponin level on admission, peak CRP, creatinine level on ICU admission, body mass index and age were found to have a high relative importance (> 0.7). …”
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    Predictors of in-hospital mortality in critically ill patients with COVID-19: a large dual tertiary centre study
  18. 36338
    “…Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM. …”
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    Novel biomarkers identifying hypertrophic cardiomyopathy and its obstructive variant based on targeted amino acid metabolomics
  19. 36339
    por Villalba, Julian A., Hilburn, Caroline F., Garlin, Michelle A., Elliott, Grant A., Li, Yijia, Kunitoki, Keiko, Poli, Sergio, Alba, George A., Madrigal, Emilio, Taso, Manuel, Price, Melissa C., Aviles, Alexis J., Araujo-Medina, Milagros, Bonanno, Liana, Boyraz, Baris, Champion, Samantha N., Harris, Cynthia K., Helland, Timothy L., Hutchison, Bailey, Jobbagy, Soma, Marshall, Michael S., Shepherd, Daniel J., Barth, Jaimie L., Hung, Yin P., Ly, Amy, Hariri, Lida P., Turbett, Sarah E., Pierce, Virginia M., Branda, John A., Rosenberg, Eric S., Mendez-Pena, Javier, Chebib, Ivan, Rosales, Ivy A., Smith, Rex N., Miller, Miles A., Rosas, Ivan O., Hardin, Charles C., Baden, Lindsey R., Medoff, Benjamin D., Colvin, Robert B., Little, Brent P., Stone, James R., Mino-Kenudson, Mari, Shih, Angela R.
    Publicado 2022
    “…Machine-learning-derived morphometric analysis of the microvasculature was performed, with a random forest classifier quantifying vascular congestion (C(Vasc)) in different microscopic compartments. …”
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    Vasculopathy and Increased Vascular Congestion in Fatal COVID-19 and Acute Respiratory Distress Syndrome
  20. 36340
    “…Six Machine Learning (ML) models, including K-Nearest Neighbors , Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), are implemented for survival prediction in both classification and regression approaches. …”
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    Application of machine learning techniques for predicting survival in ovarian cancer
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