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  1. 36941
    por Jia, Peng, Dong, Li-feng, Tu, Yan, Diao, Qi-yu
    Publicado 2023
    “…The co-occurrence network analysis of rumen bacteria and archaea revealed that dietary treatments influenced microbial interaction patterns, with BS and MCE cows having more and stronger associations than CON cows. The random forest and heatmaps analysis demonstrated that the Halopenitus persicus was positively correlated with fat- and protein-corrected milk yield; Clostridium sp. …”
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    Bacillus subtilis and Macleaya cordata extract regulate the rumen microbiota associated with enteric methane emission in dairy cows
  2. 36942
    “…The findings showed that residual transmission may occur outdoors in the villages, and outside the villages in cultivation fields and forested areas. Epidemiological data showed that transmission was higher in surveillance sites which were targeted as part of a malaria response rather than sentinel sites. …”
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    Bionomics of malaria vectors in Lao PDR, 2018–2020: entomological surveillance as a key tool for malaria elimination
  3. 36943
    “…It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600–0.622), 0.837 (95%CI, 0.828–0.845), and 0.0.831 (95%CI, 0.821–0.845), respectively. …”
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    Online Artículo Texto
    Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study
  4. 36944
    “…Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. …”
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    Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models
  5. 36945
    “…Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion. …”
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    Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches
  6. 36946
    “…To evaluate publication bias, a forest plot, Begg’s test, and Egger’s test were employed. …”
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    Global prevalence of post-abortion depression: systematic review and Meta-analysis
  7. 36947
    “…Gradient Boosting Survival Analysis (GBSA), Random Survival Forest (RSF), and Extra Survival Trees (EST) were used to develop prognosis models, which were compared to Cox regression. …”
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    Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive
  8. 36948
    “…IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). …”
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    Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients
  9. 36949
    “…Finally the results are presented with a meta-analysis of pooled estimates, forest plots, and tables. The quantitative data were analyzed using Stata version 14. …”
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    Intestinal helminth co-infection and associated factors among pulmonary tuberculosis patients in Africa and Asia: a systematic review and meta-analysis
  10. 36950
    por Zhu, Ning, Lin, Shanhong, Cao, Chao
    Publicado 2023
    “…Kaplan-Meier (KM) curves showed that Group 2 APE patients had the highest risk of all-cause mortality compared with the other two groups (log-rank test, P = 0.0004). Forest plot visualization using the Cox proportional hazard model showed a significant increase in the risk of 30-day all-cause mortality by 239% (hazard ratio [HR] = 3.385 [1.115–10.273], P = 0.031) and 338% (HR = 4.377 [1.228–15.598], P = 0.023), and the trend test showed a statistical difference (P = 0.042). …”
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    A novel prognostic prediction indicator in patients with acute pulmonary embolism: Naples prognostic score
  11. 36951
    “…Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO-AKI. …”
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    Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery
  12. 36952
    “…The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. …”
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    Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm
  13. 36953
    “…Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. …”
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    Non-alcoholic fatty liver disease risk prediction model and health management strategies for older Chinese adults: a cross-sectional study
  14. 36954
    “…We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. …”
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    Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study
  15. 36955
    “…We then trained and optimized our model using random forest (RF), extreme gradient boosting, light gradient boosting machine, and logistic regression models. …”
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    Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation
  16. 36956
    “…These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. …”
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    Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy
  17. 36957
    “…We employed machine learning algorithms—random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression—to select key hub genes. …”
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    Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning
  18. 36958
    “…METHODS: We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. …”
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    Using verbal autopsy to measure causes of death: the comparative performance of existing methods
  19. 36959
    “…BACKGROUND: The New Forest Parenting Programme (NFPP) is a home-delivered, evidence-based parenting programme to target symptoms of attention-deficit/hyperactivity disorder (ADHD) in preschool children. …”
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    Study protocol for a randomized controlled trial comparing the efficacy of a specialist and a generic parenting programme for the treatment of preschool ADHD
  20. 36960
    “…Increased concentrations of greenhouse gases, and especially carbon dioxide (CO(2)), in the atmosphere have already warmed the planet substantially, causing more severe and prolonged heat waves, variability in temperature, increased air pollution, forest fires, droughts, and floods – all of which can put the respiratory health of the public at risk. …”
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    Meteorological conditions, climate change, new emerging factors, and asthma and related allergic disorders. A statement of the World Allergy Organization
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