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  1. 36781
    “…Nested cross-validated random forest classifier identified the 10 most important genera (Lactobacillus, Escherichia, Bifidobacterium, Capnocytophaga, Bacteroidetes_[G-7], Parvimonas, Bacteroides, Klebsiella, Lautropia, and Prevotella) that could differentiate OSA children from controls with an area under the curve (AUC) of 0.94. …”
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    Characteristics of salivary microbiota in children with obstructive sleep apnea: A prospective study with polysomnography
  2. 36782
    “…Certainty of evidence was assessed as per the GRADE criteria. Forest plots were constructed to assess the effect size and corresponding 95% CIs using fixed-effect models, and random-effect models were employed when required. …”
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    Herbal medicine for amyotrophic lateral sclerosis: A systematic review and meta-analysis
  3. 36783
    “…The data from Miami were used to create a random forest model for predicting upgrade in sperm concentration. …”
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    Artificial Intelligence Based Machine Learning Models Predict Sperm Parameter Upgrading after Varicocele Repair: A Multi-Institutional Analysis
  4. 36784
    “…Then, eight signature genes were determined by the machine learning method of support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and artificial neural network (ANN), comprising LATS1, EHF, DUSP16, ADCK5, PATZ1, DEK, MAP2K1, and ETS2, which were also validated in the testing gene set (GSE106602). …”
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    Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning
  5. 36785
    “…The three groups corresponded to the Eurasian forest subkingdom, Asian desert flora subkingdom, and Sino‐Japanese floristic regions, respectively. …”
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    Comparative evolutionary history of two closely related desert plant, Convolvulus tragacanthoide and Convolvulus gortschakovii (Convolvulaceae) from northwest China
  6. 36786
    “…Wilson score, Random Forest, logistic regression, and Pearson’s chi-square test with bootstrap aggregation were performed for determining the perioperative risk factors for recurrence. …”
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    Covered stent treatment for arterial complications after pancreatic surgery: risk assessment for recurrence and peri-stent implantation management
  7. 36787
    “…Gene set enrichment analysis (GSEA) was used to explore the signaling pathways affected by these differentially expressed genes. The random forest algorithm was used to identify genes with the highest correlation with the iTLS in the training set. …”
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    Protective effect of tertiary lymphoid structures against hepatocellular carcinoma: New findings from a genetic perspective
  8. 36788
    “…Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. …”
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    A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning
  9. 36789
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    Replacement of dietary corn with corn bran plus condensed distillers solubles effects on feedlot growth performance and carcass trait responses of beef steers
  10. 36790
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    A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response
  11. 36791
    “…These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug‐resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow‐up. …”
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    Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy
  12. 36792
    “…By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. Another 45 patients who underwent CCTA and invasive FFR were included to assure the performance of Rad-signature. …”
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    Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia
  13. 36793
    “…We performed random-effects meta-analysis of on-admission differences between mild and M/SPAP in laboratory parameters, etiology, demographic factors, etc. calculating risk ratios (RR) or mean differences (MD) with 95% confidence intervals (CI) and created forest plots. For the meta-analysis of predictive score systems, we generated hierarchical summary receiver operating characteristic curves using a bivariate model. …”
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    Admission risk factors and predictors of moderate or severe pediatric acute pancreatitis: A systematic review and meta-analysis
  14. 36794
    “…Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. …”
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    A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making
  15. 36795
    “…Propensity score matching (PSM) analysis was used to reduce patient selection bias, and the random survival forest (RF) model was employed to explore prognostic factors affecting patient survival. …”
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    Transarterial chemoembolization combined with apatinib with or without PD-1 inhibitors in BCLC stage C hepatocellular carcinoma: A multicenter retrospective study
  16. 36796
    por Li, Yao, Zhang, Wei, Dai, Yan, Chen, Keping
    Publicado 2022
    “…The HCM-related m6A regulators were selected using support vector machine recursive feature elimination and random forest algorithm. A significant gene signature was then established using least absolute shrinkage and selection operator and then verified by GSE130036. …”
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    Identification and verification of IGFBP3 and YTHDC1 as biomarkers associated with immune infiltration and mitophagy in hypertrophic cardiomyopathy
  17. 36797
    “…Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). …”
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    A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses
  18. 36798
    “…The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. …”
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    Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation
  19. 36799
    “…Four ML models (LR, random forest, support vector machine, and k-nearest neighbor) and the corresponding feature ranks were conducted. …”
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    Machine Learning for the Prevalence and Severity of Coronary Artery Calcification in Nondialysis Chronic Kidney Disease Patients: A Chinese Large Cohort Study
  20. 36800
    “…We used a primary care electronic health record dataset derived from the UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic machine learning classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees. …”
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    Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools
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