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  1. 36381
    “…Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. …”
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    Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model
  2. 36382
    “…From the heart–dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. …”
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    Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer
  3. 36383
    por Wakoya, Reta, Afework, Mekbeb
    Publicado 2023
    “…The heterogeneity of studies was determined using the Cochrane Q test statistic and I(2) test statistics with forest plots. A random effects model was used to examine the pooled burden of neural tube defects, subgroups of the region, subtypes of NTDs, sensitivity analysis, and publication bias. …”
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    Burden of Neural Tube Defects and Their Associated Factors in Africa: A Systematic Review and Meta-Analysis
  4. 36384
    “…We derived environmental variables from fine-resolution RS data (Landsat 8) and examined a variable distance radius (1–5 km) for aggregating these variables around point-prevalence locations in a non-parametric random forest modeling approach. We used partial dependence and individual conditional expectation plots to improve interpretability of results. …”
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    How do disease control measures impact spatial predictions of schistosomiasis and hookworm? The example of predicting school-based prevalence before and after preventive chemotherapy in Ghana
  5. 36385
    “…For every 1 mmHg/min reduction in the rate of decline of SBP, the respective aORs for DGF were 0.95 (95% CI 0.91–0.99) and 0.98 (95% CI 0.93–1.0) in the random forest and least absolute shrinkage and selection operator models. …”
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    Trajectories of systolic blood pressure decline in kidney transplant donors prior to circulatory death and delayed graft function
  6. 36386
    “…Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. …”
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    Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability
  7. 36387
    “…We performed the receiver operating characteristic (ROC) analysis to compare the performance of four models, and we found that the Random Forest (RF) model had the highest AUC value of 1.000. …”
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    Identification and prediction of m7G-related Alzheimer’s disease subtypes: insights from immune infiltration and machine learning models
  8. 36388
    por Jiang, Zaiju, Yang, Shaozhang, Luo, Sha
    Publicado 2023
    “…Absolute Factor Score/Multiple Linear Regression (APCS/MLR), geographic information system (GIS), self-organizing mapping (SOM), and random forest (RF) are used for the source allocation of soil heavy metals. …”
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    Source analysis and health risk assessment of heavy metals in agricultural land of multi-mineral mining and smelting area in the Karst region – a case study of Jichangpo Town, Southwest China
  9. 36389
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    The Difference in Clinical Behavior of Gene Fusions Involving RET/PTC Fusions and THADA/IGF2BP3 Fusions in Thyroid Nodules
  10. 36390
    “…The median campaign-calculated OHr from VOCs measured at TFS was 0.7 s(−1), roughly 5 % of the values typically reported in lower-latitude forested ecosystems. Ten species account for over 80 % of the calculated VOC OHr, with formaldehyde, isoprene and acetaldehyde together accounting for nearly half of the total. …”
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    Atmospheric biogenic volatile organic compounds in the Alaskan Arctic tundra: constraints from measurements at Toolik Field Station
  11. 36391
    “…METHODS: We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. …”
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    Prediction of short-term atrial fibrillation risk using primary care electronic health records
  12. 36392
    “…The first approach applies a second CNN to the segmentation output to predict the Koos grade, the other approach extracts handcrafted features which are passed to a Random Forest classifier. The pipeline results were compared to those achieved by two neurosurgeons. …”
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    Automated Koos Classification of Vestibular Schwannoma
  13. 36393
    “…Data were gathered as part of the Dundrum Forensic Redevelopment Evaluation Study (D-FOREST). (Davoren et al., BMJ Open (2022) 12(7): e058581) RESULTS: During the 68-months there were 76 admissions. …”
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    Admissions to the National Forensic Mental Health Service, Central Mental Hospital Dundrum, before, during and after the COVID-19 pandemic: changes in the need for security and urgency of need for admission
  14. 36394
    “…Using standard meta-analysis methods we will generate forest plots showing prevalence and 95% CIs for each study. …”
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    P23 Prevalence of asymptomatic bacteriuria and associated risk factors in an adult population relevant to UK primary care: a systematic review and meta-analysis
  15. 36395
    “…Prediction models were built and internally validated via multinomial logistic regression (MLR) and random forest (RF). The MLR and RF model performance was compared by accuracy and the discriminability of mSI subgroups (i.e., p-value of one-sided binomial test between the accuracy and no information rate). …”
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    Long-term prediction of multidimensional social inclusion among patients with schizophrenia spectrum disorder
  16. 36396
    “…Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.g., G4 sequences, endpoints, cell lines, buffers, and assays). …”
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    G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA
  17. 36397
    “…The pooled estimates were calculated using random forest models. The risk of bias was evaluated using the RoB2 revised tool and the certainty of the evidence was assessed according to the GRADE guidelines. …”
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    Surgery vs. Radiosurgery for Patients with Localized Metastatic Brain Disease: A Systematic Review with Meta-Analysis of Randomized Controlled Trials
  18. 36398
    “…Here, we screened for AstVs, EVs, and CaVs to investigate the role of domestic animals in the emergence of zoonoses, because they are situated at the human/wildlife interface, particularly in rural forested areas in Central Africa. Rectal swabs were obtained from 123 goats, 41 sheep, and 76 dogs in 10 villages located in northeastern Gabon. …”
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    Molecular Identification of Enteric Viruses in Domestic Animals in Northeastern Gabon, Central Africa
  19. 36399
    “…DESIGN, SETTING, AND PARTICIPANTS: This cohort study evaluated 4 different machine learning approaches for estimating the likelihood of a treatment delay greater than 60 days (group least absolute shrinkage and selection operator [LASSO], bayesian additive regression tree, gradient boosting, and random forest). Criteria for selecting between approaches were discrimination, calibration, and interpretability/simplicity. …”
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    Development of a Multilevel Model to Identify Patients at Risk for Delay in Starting Cancer Treatment
  20. 36400
    “…We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). …”
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    Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis
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