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  1. 37041
    “…A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. …”
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    Online Artículo Texto
    Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study
  2. 37042
    por Muneeb, Muhammad, Henschel, Andreas
    Publicado 2021
    “…We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. …”
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    Online Artículo Texto
    Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods
  3. 37043
    “…Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. …”
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    Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation
  4. 37044
    “…Studies were classified into 10 categories as follows: (1) open flap debridement (OFD) alone versus OFD/PRF; (2) OFD/bone graft (OFD/BG) versus OFD/PRF; (3) OFD/BG versus OFD/BG/PRF; (4–6) OFD/barrier membrane (BM), OFD/PRP, or OFD/enamel matrix derivative (EMD) versus OFD/PRF; (7) OFD/EMD versus OFD/EMD/PRF; (8–10) OFD/PRF versus OFD/PRF/metformin, OFD/PRF/bisphosphonates, or OFD/PRF/statins. Weighted means and forest plots were calculated for probing depth (PD), clinical attachment level (CAL), and radiographic bone fill (RBF). …”
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    Use of platelet-rich fibrin for the treatment of periodontal intrabony defects: a systematic review and meta-analysis
  5. 37045
    “…Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. …”
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    Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting
  6. 37046
    “…With original Kinect skeleton data extracting and preprocessing, the proposed experiment demonstrated four strong machine learning tools: Support Vector Machine, Logistic Regression, Random Forest and Gradient Boosting. Using the precision, recall, sensitivity, specificity, roc-curve, confusion matrix et.al, indicators were calculated as the measurement of methods, which were commonly used to evaluate classification methodologies. …”
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    Simple action for depression detection: using kinect-recorded human kinematic skeletal data
  7. 37047
    “…Next, the integrated classifier system Rotation Forest uses “support vector machine” subclassifications to divide three types of feature spaces into several subsets. …”
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    CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques
  8. 37048
    “…Pooled prevalence was estimated using a random-effects model with a 95% confidence interval (CI) and depicted as a forest plot, while heterogeneity was evaluated using Cochran’s Q-test. …”
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    Intestinal parasites among intellectually disabled individuals in Iran: a systematic review and meta-analysis
  9. 37049
    “…A radiomics biomarker was then built with the random survival forest method. The patients with nodules were divided into low-, middle- and high-risk subgroups by two biomarker cutoffs that optimized time-dependent sensitivity and specificity for decisions about diagnostic workup within 3 months and about repeat screening after 12 months, respectively. …”
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    Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study
  10. 37050
    “…The performance of this feature selection approach was compared to that of three other approaches, with the first two based on the Random Forest technique and the other on receiver operating characteristic curve analysis. …”
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    A filter approach for feature selection in classification: application to automatic atrial fibrillation detection in electrocardiogram recordings
  11. 37051
    “…Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). …”
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    EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer
  12. 37052
    “…Classical statistical analysis revealed that treatment ineffectiveness seemed to be influenced by the male sex, which was not confirmed by the multivariate analysis using the machine learning algorithm (random forest). Coinfection with HBV (including patients with on-treatment reactivation of HBV infection) or HIV, extrahepatic manifestations, and renal failure did not significantly affect the treatment efficacy. …”
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    Factors influencing the failure of interferon-free therapy for chronic hepatitis C: Data from the Polish EpiTer-2 cohort study
  13. 37053
    “…Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. …”
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    Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study
  14. 37054
    “…With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. …”
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    Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study
  15. 37055
    “…Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. …”
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    Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer
  16. 37056
    “…Linear discriminant analysis (LDA) and random forest (RF) algorithms were used to develop the classification models. …”
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    Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity
  17. 37057
    “…Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. …”
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    Construction of a prognostic model with histone modification-related genes and identification of potential drugs in pancreatic cancer
  18. 37058
    “…We used an MLC model, based on the Random Forest (RF) technique, to leverage these correlations and predict four complications simultaneously. …”
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    Application of multi-label classification models for the diagnosis of diabetic complications
  19. 37059
    “…METHODS: This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years. …”
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    Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease
  20. 37060
    “…RESULTS: A total of 21 RCTs involving 7571 participants were included for meta-analysis. The forest plots showed that there is significant effect in global cognitive function (15 RCTs, SMD: 0.36; 95 % CI: 0.18 to 0.54, P < 0.01) and Hcy (11 RCTs, MD: -4.59; 95 %CI: -5.51 to -3.67, P < 0.01), but there is no effect in information processing speed (10 RCTs, SMD: 0.06; 95 % CI: -0.12 to 0.25, P = 0.49), episodic memory (15 RCTs, SMD: 0.10; 95 % CI: -0.04 to 0.25, P = 0.16), executive function (11 RCTs, SMD: -0.21; 95 % CI: -0.49 to 0.06, P = 0.13). …”
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    The preventive efficacy of vitamin B supplements on the cognitive decline of elderly adults: a systematic review and meta-analysis
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