“…We used balanced random forest (BRF) and sparse partial least squares-discriminant analysis (sPLS-DA) to assess classification and parameter selection, and validation was by ten-fold cross-validation. …”
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“…Machine Learning (ML): the output of the NLP BOW and Document Embedding models were fed to six different conventional machine learning systems (logistic regression, support vector machine, random forest, k-nearest neighbor clustering, a three-layer neural network, and Naïve Bayes). …”
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“…Generalized linear model (GLM) employing logistic regression and survival analysis with R software was used and the final fitting of the model was determined through the use of random forest and evolutionary tree algorithms. Individuals presenting with an [AMH] of >3.15 ng/ml and a good prognosis had a lower success per treatment (n = 11, 0% success) when total gonadotropin doses were greater than 3325 IU. …”
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“…The median age was 46 (IQR: 38-53); 68% male; 51% were white, 39% black. 1544 (13%) experienced viral rebound during follow-up. Forest plot summaries of univariable and multivariable logistic regression models are in Figures 1&2. …”
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“…Fowler, Jr., MD, MHS, Achaogen (Consultant)Actavis (Grant/Research Support)Advanced Liquid Logics (Grant/Research Support)Affinergy (Consultant, Research Grant or Support)Affinium (Consultant)Allergan (Grant/Research Support)Ampliphi Biosciences (Consultant)Basilea (Consultant, Research Grant or Support)Bayer (Consultant)C3J (Consultant)Cerexa (Consultant, Research Grant or Support)Contrafect (Consultant, Research Grant or Support)Cubist (Grant/Research Support)Debiopharm (Consultant)Destiny (Consultant)Durata (Consultant)Forest (Grant/Research Support)Genentech (Consultant, Research Grant or Support)Integrated Biotherapeutics (Consultant)Janssen (Consultant, Research Grant or Support)Karius (Grant/Research Support)Locus (Grant/Research Support)Medical Biosurfaces (Grant/Research Support)Medicines Co. …”
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“…Machine learning classifiers were trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection, with the best classification performance displayed by a random forests algorithm. A Bayesian mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the sensitivity and classification performance of serological diagnostics during the first year following symptom onset. …”
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“…Data were pooled using a random-effects model for meta-analyses and forest plots constructed to report standardized mean differences (SMDs). …”
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“…When SCGF-β was excluded, a random-forest algorithm classified and predicted asymptomatic and symptomatic cases of COVID-19 with 94.8% AUROC (area under the receiver operating characteristic) curve accuracy (95% CI 90.17%-100%). …”
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“…Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system. …”
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“…The machine learning (ML) analysis discriminated subjects from the LA and CTRL groups using the joint predictors Bacteroides 1.8E + 00 (95% CI 1.1, 2.5)%, 3.8E + 00 (95% CI 2.7, 4.8)% (p = 0.002); Prevotella 1.3 (95% CI 0.28, 2.4)%, 0.1 (95% CI 0.07, 0.3)% (p = 0.02); Intestinimonas 1.3E-02 (95% CI 9.3E-03, 1.7E-02)%, 5.9E-03 (95% CI 3.9E-03, 7.9E-03)% (p = 0.002), Subdoligranulum 7.9E-02 (95% CI 2.5E-02, 1.3E-02)%, 3.2E-02 (95% CI 1.8E-02, 4.6E-02)% (p = 0.02); and the ratio of Bacteroides to Prevotella 133 (95% CI -86.2, 352), 732 (95% CI 385, 1079.3) (p = 0.03), leading to an ROC curve with AUC of 0.94. Further, random forest ML analysis identified VO2max, BMI, and the Bacteroides to Prevotella ratio as appropriate, joint predictors for discriminating between subjects from the LA and CTRL groups. …”
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“…The diagnostic accuracy of LR-MLE was assessed together with nine ML algorithms: decision trees, random forests, extreme gradient boosting (XGBoost), support-vector machines, naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. …”
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“…The correlation between EEF1E1 expression and patients’ prognosis was analyzed in HCC, shown by forest plots, nomogram and Kaplan–Meier curves. Hazard ratio (HR) with 95% confidence intervals and log-rank p-value were calculated via multivariate/univariate survival analyses. …”
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“…Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. …”
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“…The algorithm to predict delirium was developed using patient data from the first 2 years of the study period and validated using patient data from the last 6 months. Random forest (RF), Extreme Gradient Boosting (XGBoost), deep neural network (DNN), and logistic regression (LR) were used. …”
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“…For CVP, we explored multiple models, including k-nearest-neighbor classifier (KNNC), Adaboost, support vector machine (SVM), logistic regression (LR), random forest (RF), Gaussian naïve Bayes (GNB), decision trees C4.5 (C4.5), and classification and regression trees (CART). …”
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“…As there was no evidence of impairment of national malaria control strategies, public health authorities attributed the surge to a temporal increase in gold mining activity in forested regions. However, systematic analysis of this association is lacking because of the difficulties associated with collecting reliable data for both malaria and mining. …”
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“…In total, 6 decision tree models were implemented, namely the classification and regression tree (CART), C5.0, GB, XGBoost, AdaBoost algorithm and random forest models. The Shapley additive explanations framework was applied to the two optimal decision tree models to determine relative predictor importance. …”
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“…Combining the 10 loci with nine clinicopathological characteristics, we obtained 19 important features whose association with cancer recurrence was assessed by importance score via random forests. After that, a logistic regression model was trained to calculate TAM risk-of-recurrence score (TAM RORs), which is adopted to assess a patient’s risk of recurrence after TAM treatment. …”
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“…A comprehensive set of different methods and procedures were used: forest plots and respective statistics, polynomial regression, meta-regression, subgroup influence, quality assessment, and trim-and-fill analysis. 29 studies and 11,290 individuals were assessed. …”
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