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  1. 37161
    “…Fixed-effects modeling indicated that UVPs did not increase the incidences of ICU-acquired infections, including ventilator-associated pneumonia (OR = 0.96, 95% CI 0.71–1.30, I(2) = 0%, p = 0.49), catheter-associated urinary tract infection (OR 0.97, 95% CI 0.52–1.80, I(2) = 0%, p = 0.55), and catheter-related blood stream infection (OR = 1.15, 95% CI 0.72–1.84, I(2) = 0%, p = 0.66), or ICU mortality (OR = 1.03, 95% CI 0.83–1.28, I(2) = 49%, p = 0.12). Forest plotting indicated that UVPs could reduce the lengths of ICU stays (SMD =  − 0.97, 95% CI − 1.61 to 0.32, p = 0.003). …”
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    Efficacy and safety of unrestricted visiting policy for critically ill patients: a meta-analysis
  2. 37162
    “…Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features. …”
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    Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis
  3. 37163
    “…Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. CONCLUSIONS: ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. …”
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    The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review
  4. 37164
    “…The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM). …”
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    Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models
  5. 37165
    “…Subsequently, the characteristic immune cell-related genes were identified as diagnostic biomarkers for HIV-1(+) using the random forest model (RF), support vector machine model, and generalized linear model. …”
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    Exploring the biological function of immune cell-related genes in human immunodeficiency virus (HIV)-1 infection based on weighted gene co-expression network analysis (WGCNA)
  6. 37166
    “…The machine learning analysis was performed using different novelty detection algorithms [isolation forest, local outlier factor, one-class support vector machine (oSVM)], whose performance was assessed separately and as an ensemble on the inlier and outlier test sets. …”
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    Clinical application of the “sellar barrier’s concept” for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis
  7. 37167
    “…First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. …”
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    Machine Learning Algorithms for understanding the determinants of under-five Mortality
  8. 37168
    “…The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine, eXtreme gradient boosting, artificial neural network, and decision tree ranged from 0.805 [95% confidence interval (CI): 0.258-1.352] to 0.925 (95%CI: 0.378-1.472) in the training set and from 0.794 (95%CI: 0.237-1.351) to 0.912 (95%CI: 0.355-1.469) in the testing set, respectively. …”
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    Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer
  9. 37169
    “…Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). …”
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    Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods
  10. 37170
    “…For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used. RESULTS: The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls compared with patients. …”
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    Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis
  11. 37171
    “…We used 5 machine learning models for detecting the severity of depression: kernel support vector machine (SVM), random forest, logistic regression K-nearest neighbor (KNN), and complement naive Bayes (NB). …”
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    Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques
  12. 37172
    “…We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. …”
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    Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records
  13. 37173
    “…We trained a total of 45 model combinations: (1) Three different ML models were used; logistic regression (LR), random forest (RF), extreme gradient boosting (XGB); (2) Different periods of follow-up were employed for training; 1–5 years; (3) Different time distances between data used for prediction and the time of the event (death/survival) were set; 0–4 years. …”
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    Predicting mortality in the very old: a machine learning analysis on claims data
  14. 37174
    “…Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. …”
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    Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective
  15. 37175
    “…CONCLUSIONS: Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. …”
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    High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata
  16. 37176
    “…Assessment of inconsistency was not possible as no study compared two or more vaccines directly. A forest plot for indirect comparison of various COVID-19 vaccines was obtained. …”
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    Efficacy of COVID-19 vaccines: a systematic review and network meta-analysis of phase 3 randomized controlled trials
  17. 37177
    “…The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. …”
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    Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records
  18. 37178
    “…The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. …”
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    Predicting starch content in cassava fresh roots using near-infrared spectroscopy
  19. 37179
    “…The outputs of FS method were then fed into three ML algorithms: support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to compare and identify the best feature subset for the prediction of AUD from EHRs. …”
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    Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods
  20. 37180
    “…CONCLUSIONS: A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.…”
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    Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study
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