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  1. 37461
  2. 37462
  3. 37463
    “…Results were extracted into 2 × 2 outcome tables and a meta-analysis carried out producing forest plots based on relative risk. Heterogeneity was assessed using the I(2) statistic. …”
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  4. 37464
    “…The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest–Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). …”
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  5. 37465
  6. 37466
    “…To discriminate the BRAF status, a Random Forest classifier (RF) was employed. RESULTS: A specific microbial signature distinctive for BRAF status emerged, being the BRAF-mutated cases closer to healthy controls than BRAF wild-type counterpart. …”
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  7. 37467
    “…More than 62.5% of the forest areas in Ethiopia are found in the southwest region, which have been used as a source of traditional medicine to treat different human and livestock ailments. …”
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  8. 37468
    “…Factors significantly correlated with CCD or KCD were used to create forest plots to compare their differences. RESULTS: The competing risk analysis showed that age at diagnosis, race, AJCC T/N status, radiation therapy, chemotherapy and scope of lymph node represented different relationships to CCD than to KCD. …”
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  9. 37469
    “…DEGs were tested as a tumor risk classifier with a machine learning Random Forest algorithm run with gene expression data from all TCGA-PRAD (prostate adenocarcinoma) tumors as input. …”
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  10. 37470
    “…The residential inequality was assessed by calculating the risk difference in infant mortality rates between urban and rural live births and presented using a forest plot. For the spatial patterns of infant mortality, the SaTScan version 9.6 and ArcGIS version 10.6 statistical software were used to identify the spatial patterns of infant mortality. …”
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  11. 37471
    “…Firmicutes were increased, and Bacteroidetes reduced relative to ddH2O-βgal; CCL5 was increased. The random forest algorithm at the genus level predicted vancomycin treatment with 100% accuracy but 74% and 70% for hFMT and probiotic, respectively. …”
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  12. 37472
  13. 37473
    “…The results were analyzed using two computational approaches, a generalized linear model (glm) and random forest (RF) prediction model, to classify individual specimens as either Reactive or non-reactive against the SARS-CoV-2 antigens. …”
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  14. 37474
    por Chen, Jie, Rodopoulou, Sophia, de Hoogh, Kees, Strak, Maciej, Andersen, Zorana J., Atkinson, Richard, Bauwelinck, Mariska, Bellander, Tom, Brandt, Jørgen, Cesaroni, Giulia, Concin, Hans, Fecht, Daniela, Forastiere, Francesco, Gulliver, John, Hertel, Ole, Hoffmann, Barbara, Hvidtfeldt, Ulla Arthur, Janssen, Nicole A. H., Jöckel, Karl-Heinz, Jørgensen, Jeanette, Katsouyanni, Klea, Ketzel, Matthias, Klompmaker, Jochem O., Lager, Anton, Leander, Karin, Liu, Shuo, Ljungman, Petter, MacDonald, Conor J., Magnusson, Patrik K.E., Mehta, Amar, Nagel, Gabriele, Oftedal, Bente, Pershagen, Göran, Peters, Annette, Raaschou-Nielsen, Ole, Renzi, Matteo, Rizzuto, Debora, Samoli, Evangelia, van der Schouw, Yvonne T., Schramm, Sara, Schwarze, Per, Sigsgaard, Torben, Sørensen, Mette, Stafoggia, Massimo, Tjønneland, Anne, Vienneau, Danielle, Weinmayr, Gudrun, Wolf, Kathrin, Brunekreef, Bert, Hoek, Gerard
    Publicado 2021
    “…OBJECTIVES: We investigated the associations between long-term exposure to [Formula: see text] elemental components and mortality in a large pooled European cohort; to compare health effects of [Formula: see text] components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms. METHODS: We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985–2005). …”
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  15. 37475
    “…In parallel work, survival modelling was performed using random survival forests. RESULTS: Prediction of nodal status yielded mean cross-validated AUC values of 0.735 ± 0.15 (SD) for clinical variables alone, 0.673 ± 0.16 (SD) for radiomic features only, and 0.764 ± 0.16 (SD) for radiomics and clinical features together. …”
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  16. 37476
    “…Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. …”
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  17. 37477
  18. 37478
    “…Given the key roles of P. thonningii for the people and the environment to improve household food security, agricultural productivity, and income sources and the threats to it, the need to protect it in natural forests and woodlands and optimize its uses in agroforestry systems is high. …”
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  19. 37479
    “…METHODS: Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). …”
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  20. 37480
    “…The present study therefore attempts to assess the implications of entire eco-restoration model as practiced by Department of Forest, Government of Uttarakhand in 2019. Its assessment was done by calculating restoration success index by way of considering three categories, viz., direct management measure (M), environmental desirability (E) and socio-economic feasibility (SE) considering 22 individual variables. …”
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