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  1. 36281
    “…For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. …”
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    Online Artículo Texto
    Development of glaucoma predictive model and risk factors assessment based on supervised models
  2. 36282
    “…Based on these patients’ demographic characteristics, early clinical and laboratory variables, and quantitative chest computerized tomography (CT) findings, we developed two random forest (RF) models able to predict intubation and intra-hospital mortality. …”
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    Online Artículo Texto
    Predictors of intubation and mortality in COVID-19 patients: a retrospective study
  3. 36283
    “…Four benchmark machine learning models, logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT) and a published cutting edge MMDL, were used to compare and evaluate the models with a fivefold cross-validation approach. …”
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    Online Artículo Texto
    Prediction of central venous catheter-associated deep venous thrombosis in pediatric critical care settings
  4. 36284
    “…AIM: The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation. …”
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    Online Artículo Texto
    Machine learning assisted Cameriere method for dental age estimation
  5. 36285
    “…Photobionts from Trebouxia clade I occur at the upper extent of mid-elevation forest and at some open, high-elevation sites, while Trebouxia clades A and S dominate open habitats at high elevation. …”
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    Turnover of Lecanoroid Mycobionts and Their Trebouxia Photobionts Along an Elevation Gradient in Bolivia Highlights the Role of Environment in Structuring the Lichen Symbiosis
  6. 36286
    “…Therefore, it is of great significance to improve the yield of taxol by modern biotechnology without destroying the wild forest resources. Endophytic fungus which symbiosis with their host plants can promote the growth and secondary metabolism of medicinal plants. …”
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    Endophytic fungus Pseudodidymocyrtis lobariellae KL27 promotes taxol biosynthesis and accumulation in Taxus chinensis
  7. 36287
    “…We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms. …”
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    Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department
  8. 36288
    “…Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. …”
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    Online Artículo Texto
    Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study
  9. 36289
    “…After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. …”
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    Radiomic analysis for predicting prognosis of colorectal cancer from preoperative (18)F-FDG PET/CT
  10. 36290
    por Guo, Haihong, Li, Jiao, Liu, Hongyan, He, Jun
    Publicado 2022
    “…We compared the performance of the applied SRL-LSTM model and several state-of-the-art SL and RL models in reducing the estimated in-hospital mortality and the Jaccard similarity with clinicians’ decisions. We used a random forest algorithm to calculate the feature importance of both the clinician policy and the AI policy to illustrate the interpretability of the AI model. …”
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    Online Artículo Texto
    Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study
  11. 36291
    “…The prognostic signature was constructed depending on the risk score to assess the impact of multiple genes on the prognosis, receiver operating characteristic (ROC) curves and forest plot was constructed to assess the ability to predict the prognosis and effects of clinical variables in both high- and low-risk groups. …”
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    Identification of solute carrier family genes related to the prognosis and tumor-infiltrating immune cells of pancreatic ductal adenocarcinoma
  12. 36292
    “…This study aims to compare the cattle nasal microbiome (diversity, composition and community interaction) and the abundance of BRD pathogens (by qPCR) in the nasal microbiome of Holstein steers that are apparently healthy (Healthy group, n = 75) or with BRD clinical signs (BRD group, n = 58). We then used random forest models based on nasal microbial community and qPCR results to classify healthy and BRD-affected animals and determined the agreement with the visual clinical signs. …”
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    Identification of bovine respiratory disease through the nasal microbiome
  13. 36293
    “…Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. …”
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    Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach
  14. 36294
    “…Clinical characteristics were revealed by The Cancer Genome Atlas (TCGA) data. A nomogram and forest plot were constructed based on univariate and multivariate Cox regression. …”
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    Oligoadenylate synthetases-like is a prognostic biomarker and therapeutic target in pancreatic ductal adenocarcinoma
  15. 36295
    “…To address the problem of label availability, we created a platform that rigorously assesses and minimizes label error, and used it to iteratively train a Random Forests classifier with active learning, which identifies the most informative training sample based on prediction uncertainty. …”
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    High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales
  16. 36296
    “…Paddy rice field edge (2.03) has the highest value, followed by forest-farming ecotone (1.74), streamsides (1.71) and woodland (0.48). …”
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    Traditional knowledge of edible plants used as flavoring for fish-grilling in Southeast Guizhou, China
  17. 36297
    “…In synthetic and toy tests LANDMark consistently ranked as the best classifier and often outperformed the Random Forest classifier. When trained on the full metabarcoding dataset obtained from Canada’s Wood Buffalo National Park, LANDMark was able to create highly predictive models and achieved an overall balanced accuracy score of 0.96 ± 0.06. …”
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    LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data
  18. 36298
    “…Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. …”
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    Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer
  19. 36299
    “…We used these indicators to establish a random forest model. Seven models were built through different combinations. …”
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    Observation of the cervical microbiome in the progression of cervical intraepithelial neoplasia
  20. 36300
    “…According to the Random Forest (RF) classifier, we found that dry period length, calf birth weight, and parity were the most important cow-level risk factors for the incidence of dystocia. …”
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    Identification of cow-level risk factors and associations of selected blood macro-minerals at parturition with dystocia and stillbirth in Holstein dairy cows
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