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  1. 37001
    “…They are the largest mammals capable of powered flight, and are highly mobile, which makes them key seed and pollen dispersers in forest ecosystems. However, their mobility also facilitates transmission of zoonotic diseases and brings them in conflict with humans, and so they require a precarious balancing of conservation and management concerns throughout their Old World range. …”
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    Extreme mobility of the world’s largest flying mammals creates key challenges for management and conservation
  2. 37002
    “…The median age of participants was 18 years, approximately half were female, and the majority were either village residents (46%) or forest dwellers (40%). No adverse events were reported during the study. …”
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    Evaluation of the effectiveness of topical repellent distributed by village health volunteer networks against Plasmodium spp. infection in Myanmar: A stepped-wedge cluster randomised trial
  3. 37003
    “…Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. …”
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    Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
  4. 37004
    “…The raw mean difference of HbA1c will be provided in a random effects model and presented in a forest plot. The expected limitations of this study are incomplete data, the need to contact authors, and analysis of various types of glycemic control measures accurately within the same data set. …”
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    Effectiveness of Social Cognitive Theory–Based Interventions for Glycemic Control in Adults With Type 2 Diabetes Mellitus: Protocol for a Systematic Review and Meta-Analysis
  5. 37005
    “…METHODS: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2–10 years (PfPR(2–10)) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR(2–10) across the urban landscape. …”
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    Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators
  6. 37006
    “…This species is endemic to the Andean cloud forests of the western slope of the Cordillera Oriental of Colombia, occurring only in the department of Cundinamarca within a narrow elevational range. …”
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    Distribution, habitat suitability, conservation state and natural history of endangered salamander Bolitoglossa pandi
  7. 37007
    “…Pooled estimates with its 95% confidence interval were reported using forest plots. The quality of evidence from the review was assessed using GRADE approach. …”
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    Epidemiology of preterm birth in Ethiopia: systematic review and meta-analysis
  8. 37008
    “…This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. …”
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    Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
  9. 37009
    “…Potential associations between the severity of symptoms and exercise testing parameters were analyzed using a Forest-Tree Machine Learning approach, which accomplishes for a ratio between number of observations and number of screened variables less than unit. …”
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    Dyspnea, effort and muscle pain during exercise in lung transplant recipients: an analysis of their association with cardiopulmonary function parameters using machine learning
  10. 37010
    “…Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. …”
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    Automated discretization of ‘transpiration restriction to increasing VPD’ features from outdoors high-throughput phenotyping data
  11. 37011
    “…The MVA-BN-Filo vaccine is based on a live, highly attenuated poxviral vector incapable of replicating in human cells and encodes glycoproteins of Ebola virus Zaire, Sudan virus and Marburg virus and the nucleoprotein of the Thai Forest virus. This vaccine has been approved in the European Union in July 2020 as part of a heterologous Ebola vaccination regimen. …”
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    The Brighton Collaboration standardized template for collection of key information for risk/benefit assessment of a Modified Vaccinia Ankara (MVA) vaccine platform
  12. 37012
    “…For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. RESULTS: PC analysis discriminated between benign and malignant prostate tissue. …”
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    Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features
  13. 37013
    “…To overcome the limited sample size, prediction models based on random Forest were constructed using the differentially expressed genes (DEGs) as selected features. …”
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    Prediction of prognostic signatures in triple-negative breast cancer based on the differential expression analysis via NanoString nCounter immune panel
  14. 37014
    “…RESULTS: Implemented on the data set with 44,918 individual admission episodes of intensive care, the AutoScore-created scoring models performed comparably well as other standard methods (ie, logistic regression, stepwise regression, least absolute shrinkage and selection operator, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. …”
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    AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records
  15. 37015
    “…Statistical heterogeneity will be assessed by visual inspection of forest plots and measured using the I(2) statistics. …”
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    Impact of surgical margin status on the survival outcome after surgical resection of gastric cancer: a protocol for systematic review and meta-analysis
  16. 37016
    “…In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. …”
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    Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study
  17. 37017
    “…The pooled prevalence was presented in forest plots. RESULTS: A total of 9 studies with 9785 participants were included, and the overall pooled estimated prevalence of unmet need for family planning among fecund women in Ethiopia was 34.90% (95% CI: 24.52, 45.28%). …”
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    Unmet need for family planning in Ethiopia and its association with occupational status of women and discussion to her partner: a systematic review and meta-analysis
  18. 37018
    “…Although the increased incidence of P. knowlesi cases can be attributed to various factors including improved diagnostic capacity, reduction in human malaria cases, and increase in awareness of P. knowlesi, more than 50% of P. knowlesi cases were associated with agriculture and plantation activities, with a large remainder proportion linked to forest-related activities. CONCLUSIONS: Malaysia has entered the elimination phase of malaria control. …”
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    Malaria elimination in Malaysia and the rising threat of Plasmodium knowlesi
  19. 37019
    “…A meta-analysis of DHS data of each Sub-Saharan countries was conducted to generate pooled magnitude and a forest plot was used to present it. A multilevel logistic regression model was fitted to identify determinants of postnatal care utilization. …”
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    Determinants of postnatal care utilization in sub-Saharan Africa: a meta and multilevel analysis of data from 36 sub-Saharan countries
  20. 37020
    “…To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. …”
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    Machine learning approaches classify clinical malaria outcomes based on haematological parameters
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