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  1. 36261
    “…Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps. …”
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    An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study
  2. 36262
    “…A two-level logistic model was employed to investigate the association strengths reflected by adjusted odds ratios (AOR) and 95% confidence intervals in forest plots. RESULTS: On average, 17.58% of the respondents with HBP and 14.87% with DM had experienced health services underutilization during 1 month before the survey. …”
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    Online Artículo Texto
    Health service underutilization and its associated factors for chronic diseases patients in poverty-stricken areas in China: a multilevel analysis
  3. 36263
    “…Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. …”
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    An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation
  4. 36264
    por Sitko, Jiljí, Heneberg, Petr
    Publicado 2021
    “…In Central Europe, conspicuous changes are evident in populations of common farmland birds, in strong contrast to forest birds in the same region. However, there is a lack of information on longitudinal changes in trematodes that are associated with common farmland birds, despite the fact that diversity of trematodes is directly linked to the preservation of long-established food webs and habitat use adaptations of their hosts. …”
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    Long-term dynamics of trematode infections in common birds that use farmlands as their feeding habitats
  5. 36265
    “…Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. …”
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    Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China
  6. 36266
    “…Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. …”
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    A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants
  7. 36267
    por Liu, Siru, Li, Jili, Liu, Jialin
    Publicado 2021
    “…The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. …”
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    Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis
  8. 36268
    “…A random-effects model was used to estimate the pooled estimate with a 95% confidence interval (CI). Forest plots were used to visualize the presence of heterogeneity and estimate the pooled burden and determinants of chronic kidney disease among diabetic patients. …”
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    Burden and Determinants of Chronic Kidney Disease Among Diabetic Patients in Ethiopia: A Systematic Review and Meta-Analysis
  9. 36269
    “…Our best performing model, a random forest trained by the WGS dataset, identified a species (Bacteroides coprocola) that predominantly contributes to the abundance of leuB, a gene involved in branched chain amino acid biosynthesis; a risk factor for glucose intolerance, insulin resistance, and type 2 diabetes. …”
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    Comparison of 16S and whole genome dog microbiomes using machine learning
  10. 36270
    “…The OPLS-DA and random forest (RF) analysis were used to screen the candidate lipids. …”
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    Higher Serum Lysophosphatidic Acids Predict Left Ventricular Reverse Remodeling in Pediatric Dilated Cardiomyopathy
  11. 36271
    “…When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. …”
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    A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data
  12. 36272
    “…Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. …”
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    Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma
  13. 36273
    “…This study includes demographic data, BMI, blood tests, and questionnaires before obesity treatment that cover three main areas: gastrointestinal symptoms and eating habits, physical activity and quality of life, and psychological health. We used random forest, with conditional variable importance, to study the relative importance of roughly 100 predictors of BMI, covering 15 domains. …”
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    The BAriatic surgery SUbstitution and nutrition (BASUN) population: a data-driven exploration of predictors for obesity
  14. 36274
    “…Adult mosquitoes were collected from cowsheds, lakesides, shrubs, and habitats ranging from open grassland to coniferous forest using a Centers for Disease Control and Prevention (CDC) miniature light trap enhanced with dry ice, aspirators, and sweeping nets. …”
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    Updated distribution of anopheline mosquitoes in Hokkaido, Japan, and the first evidence of Anopheles belenrae in Japan
  15. 36275
    “…The association between serum lipids and NAFLD was investigated using analysis of covariance, random forest analysis, and by generating receiver operator characteristic (ROC) curves. …”
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    Serum lipids are associated with nonalcoholic fatty liver disease: a pilot case-control study in Mexico
  16. 36276
    “…Six machine learning algorithms (stochastic gradient boosting, random forest, neural network, support vector machine, adaptive gradient boosting, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and internally validated on the independent set of patients. …”
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    Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction
  17. 36277
    “…Conditional average treatment effects (CATE) and 95% confidence intervals were computed from causal forest including 85 clinical and demographic variables. …”
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    Online Artículo Texto
    Effectiveness of oseltamivir treatment on clinical failure in hospitalized patients with lower respiratory tract infection
  18. 36278
    “…We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. …”
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    Validated tool for early prediction of intensive care unit admission in COVID-19 patients
  19. 36279
    “…Finally, machine learning (ML) techniques including neural networks, extreme gradient boosting, random forest, support vector machine, and decision tree are implemented for both approaches to predict LOS of patients admitted to the Emergency Department of Odense University Hospital between June 2018 and April 2019. …”
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    Quantifying the impact of addressing data challenges in prediction of length of stay
  20. 36280
    “…METHODS: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. …”
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    Predicting COVID-19–Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development Study
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