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  1. 37061
    “…We could not find enough studies for other adverse health outcomes to pool data for a Forest plot. CONCLUSIONS: The odds of low birth weight were found to be high in both asthma and cerebral palsy. …”
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    Low birth weight as a predictor of adverse health outcomes during adulthood in twins: a systematic review and meta-analysis
  2. 37062
    “…Also, the most important predictor for tick abundance was the effect of the extent of arable land (negative), while forests and grasslands contributed less, with no effect observed in case of urbanized areas and watercourses. …”
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    Five ixodid tick species including two morphotypes of Rhipicephalus turanicus on nestlings of Eurasian eagle owl (Bubo bubo) from south-eastern Bulgaria
  3. 37063
    “…Analysis of the interviews showed that gold miners were subjected to prejudice from the community due to forest diseases that they can transmit, and their activities are often associated with crime. …”
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    Perceptions about malaria among Brazilian gold miners in an Amazonian border area: perspectives for malaria elimination strategies
  4. 37064
    “…Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). …”
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    Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt
  5. 37065
    “…On conversion of evergreen forests (EF) to upland agriculture (Shifting—SC and Settled—SA) and plantation (PH), Mn, Cu, and Zn concentrations decreased significantly from 30.5, 1.74, and 2.13 mg kg(−1) to 6.44–17.8, 0.68–0.81, and 1.06–1.42 mg kg(−1), respectively. …”
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    Effect of land-use change along altitudinal gradients on soil micronutrients in the mountain ecosystem of Indian (Eastern) Himalaya
  6. 37066
    “…Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets. …”
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    Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery
  7. 37067
    “…Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. …”
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    N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis
  8. 37068
    “…For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. …”
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    Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning
  9. 37069
    “…Raw accelerometer data were processed into daily time spent in sedentary activities, light-intensity activities and games, walking, running, and moderate-to-vigorous activities and games using a random forest (RF) PA classification algorithm specifically developed for children. …”
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    Fundamental movement skill proficiency and objectively measured physical activity in children with bronchiectasis: a cross-sectional study
  10. 37070
    por Seifu, Wubareg, Mekonen, Liyew
    Publicado 2021
    “…Heterogeneity was assessed using I(2) statistics while publication was assessed through funnel plot. Forest plot were used to present the pooled prevalence with a 95% confidence interval (CI) using the random effect model. …”
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    Breast self-examination practice among women in Africa: a systematic review and Meta-analysis
  11. 37071
    “…A total of 23 putative prognostic factors were chosen and then selected using a random survival forest (RSF). The model performance in estimating PFS probability was evaluated by assessing calibration (internally—developing set and externally—validating set) and quantifying the discriminative ability (Harrell C index). …”
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    Nomogram to predict the outcomes of patients with microsatellite instability-high metastatic colorectal cancer receiving immune checkpoint inhibitors
  12. 37072
    “…We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). …”
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    Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study
  13. 37073
    “…Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. …”
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    Distinct transcriptional programs stratify ovarian cancer cell lines into the five major histological subtypes
  14. 37074
    “…A single randomly selected liver metastasis sample from each patient was used for unsupervised subtype discovery by nonnegative matrix factorization, and a random forest prediction model was trained to classify multi-metastatic samples, as well as liver metastases from two independent series of 308 additional patients. …”
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    De novo transcriptomic subtyping of colorectal cancer liver metastases in the context of tumor heterogeneity
  15. 37075
    “…A substantial portion of the direct visual documentation showed only a partial overlap, especially for the items of size and color, thereby prohibiting the statistical comparison of these two items. A forest plot analysis of the remaining six items showed no significant difference between the methods. …”
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    Comparison of superficial wound documentation using 2D forensic photography, 3D photogrammetry, Botscan© and VR with real-life examination
  16. 37076
    “…Gene expression data (GSE17536 and GSE39582) were downloaded from the Gene Expression Omnibus as a validating set. A random forest algorithm was used to score the risk for every case. …”
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    Interferon regulatory factor family influences tumor immunity and prognosis of patients with colorectal cancer
  17. 37077
    “…We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. …”
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    Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment
  18. 37078
    “…The expression differences based on various clinicopathological features were evaluated using subgroup analysis and forest map analysis. The regulatory mechanism of MKRN3 was further investigated using gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. …”
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    Elevated expression of MKRN3 in squamous cell carcinoma of the head and neck and its clinical significance
  19. 37079
    “…Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. RESULTS: Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. …”
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    Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand
  20. 37080
    “…Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. …”
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    Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
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