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  1. 37521
    “…FINDINGS: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. …”
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    Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study
  2. 37522
    “…The accuracy of fracture classification by AI was 86–98.5 and AUC was 0.873–1.0. The forest plot represented that the mean AI diagnosis accuracy was 0.92, the mean AI diagnosis AUC was 0.969, the mean AI classification accuracy was 0.914, and the mean AI classification AUC was 0.933. …”
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    Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review
  3. 37523
    “…The results are shown in the forest plots as weighted mean differences (WMDs) with 95% confidence intervals (95% CIs). …”
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    Comparison of sealer penetration of sonic activation versus conventional needle irrigation: a systematic review and meta-analysis of randomized controlled trials
  4. 37524
    “…Binomial regression (BR), random forest, and XGBoost MLMs were used for prediction. …”
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    Risk stratification of patients who present with chest pain and have normal troponins using a machine learning model
  5. 37525
    “…The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). …”
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    Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review
  6. 37526
    “…Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. …”
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    Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran
  7. 37527
    “…We trained, tuned, and evaluated linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate [Formula: see text] BP in a nested leave-one-subject-out cross-validation framework. …”
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    Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
  8. 37528
    “…We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models. …”
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    Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study
  9. 37529
    “…Multiple machine learning algorithms were tested, including K-nearest neighbor, naive Bayes, support vector machines, random forest, extreme gradient boosting (XGBoost), and neural networks. …”
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    Machine Learning–Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study
  10. 37530
    “…After extraction and selection of the optimal radiomics features from training cohort, six machine learning (ML) classifiers including naïve Bayes (NB), random forest (RF), logistic regression (LR), linear support vector machine (L.SVM), radial SVM (R.SVM), and an artificial neural network (ANN) were developed to predict successful recanalization with SR treatment and compared. …”
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    Radiomics-based intracranial thrombus features on preoperative noncontrast CT predicts successful recanalization of mechanical thrombectomy in acute ischemic stroke
  11. 37531
    “…A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). …”
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    Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study
  12. 37532
    “…Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. …”
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    Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation
  13. 37533
    “…High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes. …”
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    Identification of gene profiles related to the development of oral cancer using a deep learning technique
  14. 37534
    “…Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information. …”
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    Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients
  15. 37535
    “…We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients’ satisfaction. …”
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    Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media
  16. 37536
    “…We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. …”
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    No-show Prediction Model Performance Among People With HIV: External Validation Study
  17. 37537
    “…Risk of bias was assessed using funnel plots and quality was assessed by the Newcastle–Ottawa Scale. Forest plots of HRs and their 95% CIs for outcome indicators were plotted. …”
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    Influence of surgical margin width on survival rate after resection of intrahepatic cholangiocarcinoma: a systematic review and meta-analysis
  18. 37538
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    Early Outcomes of Adult Heart Transplantation From COVID-19 Infected Donors
  19. 37539
    “…For each LP/PV and for VUS we applied the Mutscore algorithm using a random forest approach. The Mutscore, which integrates already existing predictive algorithms to data concerning variant topographic localization, has been already validated. …”
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    Cardiogenetics and Artificial Intelligence: the Mutscore Algorithm
  20. 37540
    “…The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. …”
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    Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
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