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  1. 37321
    “…A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning–based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. …”
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    Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort
  2. 37322
    “…Uterine artery Doppler (UtAD) notching was assigned a rank (0=absent, 1=unilateral, 2=bilateral). Random forest (RF) is a machine learning approach that generates many independent, uncorrelated decision trees based on multiple variables. …”
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    Antenatal Markers Related to Fetal Growth Restriction Can Predict Childhood Systolic Blood Pressure
  3. 37323
    “…Single-arm meta-analyses were conducted for IVMP using the DerSimonian-Laird random-effects models and forest plots were generated. Pooled means and corresponding 95% confidence intervals (CIs) were calculated. …”
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    Meta-Analysis of Proptosis Response in Thyroid Eye Disease: Efficacy of EUGOGO Recommended Treatment Regimen With IV Methylprednisolone
  4. 37324
    “…Controls included random people 60 years and above or similar aged individuals who queried for one of eight control conditions: myocardial infarction, atrial fibrillation, hypertension, migraine, B12 deficiency, depression, hypothyroidism and surgery. We used a random forest model with 1000 trees to distinguish the patient cohort from each of the control cohorts. …”
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    The First Predictor of Impending Stroke: An Internet Search-Based Algorithm Blind to Established Cardiometabolic Risk, Outperforms Classical Risk Factor-Based Calculators
  5. 37325
    “…A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size. …”
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    Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review
  6. 37326
    “…A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. …”
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    Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy
  7. 37327
    “…Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). …”
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    Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid
  8. 37328
    “…The development cohort was used to train 3 machine learning models to predict GIB at 6 and 12 months: regularized Cox proportional hazards regression (RegCox), random survival forests (RSF), and extreme gradient boosting (XGBoost). …”
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    Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment
  9. 37329
    “…With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. …”
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    Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
  10. 37330
    “…Four modelling techniques to predict individual animal intake were examined, based on (i) individual animal TOTFEEDTIME relative expressed as a proportion of the dietary group (GRP) and total GRP intake, (ii) multiple linear regression (REG) (iii) random forests (RF) and (iv) support vector regressor (SVR). …”
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    Predicting feed intake using modelling based on feeding behaviour in finishing beef steers
  11. 37331
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    Health promotion interventions for African Americans delivered in U.S. barbershops and hair salons- a systematic review
  12. 37332
    “…Our ML algorithms contained decision tree, random forest, naive Bayes, and logistic regression with least absolute shrinkage and selection operator. …”
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    Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach
  13. 37333
    “…The most common machine learning methods used were artificial neural networks (14 studies), random forest (6 studies), support vector machine (5 studies), and gradient boosting (2 studies). …”
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    Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review
  14. 37334
    “…In analysis of the recognition angle, the kNN classifier and Random Forest classifier achieved the highest average prediction accuracy on the data set established from the sound signals filtered by Wiener filtering, which were 88.83% and 88.69%, respectively. …”
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    Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives
  15. 37335
    “…We utilized Poisson regression to calculate HIV incidence rates. A survival random forest model identified the most predictive risk factors for HIV sero-conversion and then used in a survival regression tree model to elucidate hazard ratios for individuals with groups of selected risk factors. …”
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    Risk Factors for HIV sero-conversion in a high incidence cohort of men who have sex with men and transgender women in Bangkok, Thailand
  16. 37336
    “…Meta‐analyses were conducted to quantify the association between RDI levels and overall survival (OS) among studies reporting a hazard ratio (HR) for OS by similar tumor types, regimens, and RDI. Forest plots represented summary HR and 95% confidence interval (CI); Cochran's Q and I(2) tests evaluated study heterogeneity. …”
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    Relative Dose Intensity of Chemotherapy and Survival in Patients with Advanced Stage Solid Tumor Cancer: A Systematic Review and Meta‐Analysis
  17. 37337
    “…The mean, standard deviation, sensitivity, and specificity of each parameter were documented. Forest plots were constructed to display the estimated standardized mean differences (SMDs) from each included study and the overall calculations. …”
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    Diagnostic Accuracy of Female Pelvic Ultrasonography in Differentiating Precocious Puberty From Premature Thelarche: A Systematic Review and Meta-analysis
  18. 37338
    “…More than 54 % of farmers did not keep sows in pens before the farrowing, and 53 % of sows gave birth near forests. In conclusion, the village locations and rearing systems did not influence the reproductive performance of indigenous pigs in northern Laos. …”
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    Reproductive performance of indigenous Lao pigs reared by small-scale farmers in northern provinces of Laos
  19. 37339
    “…Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. …”
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    The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study
  20. 37340
    “…The pooled prevalence of Plasmodium spp. infection among patients infected with COVID-19 was estimated using the random effect model and then graphically presented as forest plots. The heterogeneity among the included studies was assessed using Cochrane Q and I(2) statistics. …”
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    Prevalence and characteristics of malaria among COVID-19 individuals: A systematic review, meta-analysis, and analysis of case reports
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