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  1. 37101
    “…The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. …”
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    Clinical evaluation of two AI models for automated breast cancer plan generation
  2. 37102
    “…Accelerometer data were temporally aligned with corresponding video footage and labelled with one of 17 behaviours. Six separate random forest models were run (three per device type) to determine the categorisation accuracy for behaviours at a fine, medium, and coarse resolution. …”
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    Categorising cheetah behaviour using tri-axial accelerometer data loggers: a comparison of model resolution and data logger performance
  3. 37103
    “…A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). …”
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    Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma
  4. 37104
    por Peng, Wenjuan, Sun, Yuan, Zhang, Ling
    Publicado 2022
    “…The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. …”
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    Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods
  5. 37105
    “…The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). …”
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    Comparing different machine learning techniques for predicting COVID-19 severity
  6. 37106
    “…By L1-penalized regression model and random forest approach, diastolic pressure gradient (DPG) and mixed venous oxygen saturation (SvO(2)) were the hemodynamic predictors most strongly associated with mortality (coefficient: 0.0255 and -0.0176, respectively), with consistent significance after adjusted for SHFM [DPG: HR 1.067, 95% CI 1.024–1.113, P = 0.022; SvO(2): HR 0.969, 95% CI 0.953–0.985, P = 0.002] or MAGGIC (DPG: HR 1.069, 95% CI 1.026–1.114, P = 0.011; SvO(2): HR 0.970, 95% CI 0.954–0.986, P = 0.004) scores. …”
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    Risk prediction in pulmonary hypertension due to chronic heart failure: incremental prognostic value of pulmonary hemodynamics
  7. 37107
    “…We used a liana Actinidia eriantha, which occurs across the eastern moist evergreen broad-leaved forests of subtropical China, as a case study to test hypotheses of refugia along the oceanic–continental gradient and ‘oceanic’ adaptation. …”
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    Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot
  8. 37108
    “…CONCLUSION: Health education focused on high-risk populations, such as migrant workers and forest goers, should be strengthened. Verbal communication and information transmission via the internet, radio, and mobile phone platforms may be required during the COVID-19 pandemic. …”
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    Asymptomatic malaria infection at the China-Vietnam border: Knowledge and implications for the cross-border migrant population during the COVID-19 pandemic
  9. 37109
    “…METHODS: Medical records of patients who underwent immunotherapy with durvalumab, ipilimumab, nivolumab, and pembrolizumab at Wake Forest Baptist Health were reviewed. We collected retrospective data regarding sex, cancer type, age, and cardiovascular disease risk factors and medications. …”
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    Cardiotoxicity risk factors with immune checkpoint inhibitors
  10. 37110
    “…We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. …”
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    Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach
  11. 37111
    “…CONCLUSION: The inhabitants of district Jammu had good knowledge of WEM, but no documentation, lying of most of the information with elders and uneducated people, and destruction of forests and other natural habitats of WEM pose a serious threat of losing this valuable information in near future. …”
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    Ethnomycological study of wild edible and medicinal mushrooms in district Jammu, J&K (UT), India
  12. 37112
    “…The results outperformed the common ensemble algorithms of AdaBoost, EasyEnsemble, and Random Forest (RF) as well as the single machine learning (ML) methods of logistic regression, decision tree, k nearest neighbors (KNN), back propagation neural network (BP) and SVM. …”
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    Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
  13. 37113
    “…We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. …”
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    Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting
  14. 37114
    “…For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. …”
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    An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification
  15. 37115
    “…The ecological roles of bacteriophages in aquatic and forest environments have been widely explored, but those in agroecosystems remains limited. …”
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    Heterogeneity of soil bacterial and bacteriophage communities in three rice agroecosystems and potential impacts of bacteriophage on nutrient cycling
  16. 37116
    “…Survival of the identified subgroups was analysed, and robustness of the selected CART variables was assessed by random forest analysis (1000 trees). RESULTS: In patients not treated with ACT, prognosis was estimated best based on expression of KCNQ1. …”
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    KCNQ1 and lymphovascular invasion are key features in a prognostic classifier for stage II and III colon cancer
  17. 37117
    “…Conditional inference random forests were used to rank the importance of chronic conditions in predicting hospitalization and potentially avoidable hospitalization separately for NH White and NH Black beneficiaries. …”
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    The importance of chronic conditions for potentially avoidable hospitalizations among non-Hispanic Black and non-Hispanic White older adults in the US: a cross-sectional observational study
  18. 37118
    “…(in J Evol Biol 26:1866–1874, 2013) report, our results indicate that no statistically significant pattern of phylogenetic covariation exists in our Trithemis forewing and hindwing data and that both male and female wing datasets exhibit substantial shape differences between species that inhabit open and forested landscapes and species that hunt over temporary/standing or running water bodies. …”
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    What you sample is what you get: ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered
  19. 37119
    “…This information is also useful for understanding the potential long-term consequences for forest dynamics resulting from population declines or extinction, and so can aid in the development of evidence-based ecological management strategies. …”
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    Body-size dependent foraging strategies in the Christmas Island flying-fox: implications for seed and pollen dispersal within a threatened island ecosystem
  20. 37120
    “…Different models based on the Sequential Organ Failure Assessment (SOFA) score, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were derived as baseline models that included admission variables only. …”
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    Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation
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