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  1. 37741
    “…We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. …”
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    High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms
  2. 37742
    “…Models based on extreme gradient boosting (XGB), gradient boosting machine, random forest, support vector machine, and Elastic Net logistic regression were trained. …”
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    Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study
  3. 37743
    “…Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. …”
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    Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study
  4. 37744
    “…Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis. …”
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    Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study
  5. 37745
    “…We then trained a Random Forest machine learning model to classify subjects with abnormal LVG and calculated SHAP values to perform feature importance and interaction. …”
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    Detection of left ventricular hypertrophy on the ECG through machine learning with a focus on obesity
  6. 37746
    “…After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. …”
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    A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology
  7. 37747
    “…We compared our results from CLOUD with APi-TOF measurements of $NH_3–H_2SO_4$ anion clusters during new-particle formation in the Finnish boreal forest. However, the exact role of $NH_3–H_2SO_4$ clusters in boundary layer particle formation remains to be resolved.…”
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    On the composition of ammonia–sulfuric-acid ion clusters during aerosol particle formation
  8. 37748
    “…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”
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    Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma
  9. 37749
    por Hung, Jason, Chen, Jackson, Chen, Olivia
    Publicado 2023
    “…Beyond the discussion from the forest plot, when looking at the single study addressing the relationship between being left-behind and having suicide attempts (note: LBC—OR is 1.22; 95 percent CI is 1.22 –and NLBC—OR is 1.42; 95 percent CI is 1.09–1.86 –at the p-value of 0.34), the findings demonstrate that such a relationship per se is not gendered at the 0.05 statistical significance level. …”
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    Are the relationships between mental health issues and being left-behind gendered in China: A systematic review and meta-analysis
  10. 37750
    “…Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. …”
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    Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands
  11. 37751
    “…Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. …”
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    Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review
  12. 37752
    “…Primary treatment outcomes having data from three or more studies available were summarized in forest plots using RevMan 5.4.1 software (The Cochrane Collaboration, Copenhagen, Denmark). …”
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    Poster 357: Tranexamic Acid in Foot and Ankle Surgery: A Systematic Review and Meta-analysis
  13. 37753
    “…Subsequently, machine learning methods, including a Linear Regression (LR), a Random Forest (RF), a Gradient Boosted Machine (GBM), and an Extreme gradient boosting (XGBOOST) model was applied to construct a model to analyze the main influencing factors of soil-transmitted helminthiasis. …”
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    Soil-transmitted helminthiasis in mainland China from 2016 to 2020: a population-based study
  14. 37754
    “…Machine learning classifiers (least absolute shrinkage and selection operator–penalized logistic regression and random forest) were then trained and tested using codified health record data (eg, child sociodemographics, medications, disposition, and laboratory testing) and the gold standard classification. …”
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    Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches
  15. 37755
    “…Annual average MDA8 O(3) and PM(2.5) at individual residential addresses were estimated by an iterative random forest model and a satellite-based spatiotemporal model, respectively. …”
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    Joint Effects of Long-Term Exposure to Ambient Fine Particulate Matter and Ozone on Asthmatic Symptoms: Prospective Cohort Study
  16. 37756
    “…BACKGROUND: The impacts of climate change, such as increased soil dryness and nutrient deficiency, highlight the need for environmentally sustainable restoration of forests and groundwater resources. However, it is important to consider that extensive afforestation efforts may lead to a depletion of groundwater supply due to higher evapotranspiration rates, exacerbating water scarcity issues. …”
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    Leaf morpho-physiological traits of Populus sibirica and Ulmus pumila in different irrigation regimes and fertilizer types
  17. 37757
    “…Employing a variety of machine learning techniques, including one‐way logistic regression, LASSO regression, random forest and artificial neural networks, we screened 11 signature genes from the intersection of immune‐associated DEGs and secretory protein‐encoding genes derived from the Human Protein Atlas. …”
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    Demystifying the impact of prenatal tobacco exposure on the placental immune microenvironment: Avoiding the tragedy of mending the fold after death
  18. 37758
    por Yu, Haiyang, Ji, Xiaoyu, Ouyang, Yang
    Publicado 2023
    “…To identify the key genes in the unfolded protein response, we constructed diagnostic models using both random forest and support vector machine-recursive feature elimination methods. …”
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    Unfolded protein response pathways in stroke patients: a comprehensive landscape assessed through machine learning algorithms and experimental verification
  19. 37759
    “…Of all treatment groups, calves fed Ca–FO achieved the highest final body weight and showed the greatest feed efficiency. Random forest analysis revealed that eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and arachidonic acid were the serum levels of FA most affected by the diets. …”
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    Effects of fat source in calf starter on growth performance, blood fatty acid profiles, and inflammatory markers during cold season
  20. 37760
    “…Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. …”
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    A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation
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