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  1. 37421
    “…The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. …”
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    AUD-DSS: a decision support system for early detection of patients with alcohol use disorder
  2. 37422
    “…We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). …”
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    Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing
  3. 37423
    “…The survey across all countries employed a cross-sectional study design and collected data on basic sociodemographic characteristics and different health indicators. Forest plot was used to present the overall and country-level prevalence of suboptimal birth spacing. …”
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    Suboptimal birth spacing practice and its predictors among reproductive-age women in Sub-Saharan African countries: a multilevel mixed-effects modeling with robust Poisson regression
  4. 37424
    “…Identification of differentially expressed genes (DEGs) and key modules via weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning algorithms (Random Forest and LASSO regression) were used to identify hub genes for diagnosing AD with MS. …”
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    Identification of co-diagnostic effect genes for aortic dissection and metabolic syndrome by multiple machine learning algorithms
  5. 37425
    “…The corresponding results, forest and funnel plots of the psychological consequences of COVID-19 were synthesized. …”
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    A meta-analysis of systematic reviews and meta-analyses to evaluate the psychological consequences of COVID-19
  6. 37426
    “…We also calculated the cumulative VTE recurrence rate at different time intervals. Forest plots were mapped and the results were summarized by the median and 95% credible interval (CIs). …”
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    The risk of recurrent venous thromboembolism after discontinuation of anticoagulant therapy in patients with cancer-associated thrombosis: a systematic review and meta-analysis
  7. 37427
    “…This study was deemed exempt from Wake Forest University Health Sciences institutional review board review (IRB00088703). …”
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    Local Culture and Community Through a Digital Lens: Viewpoint on Designing and Implementing a Virtual Second Look Event for Residency Applicants
  8. 37428
    “…Additional analysis using random forest machine learning showed that rumen fluid microbiota and their metabolites of young goats, such as Prevotellaceae UCG-003, acetate to propionate ratio could be potential microbial markers that can potentially classify high or low ADG goats with an accuracy of prediction of > 81.3%. …”
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    Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats
  9. 37429
    “…Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. …”
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    Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis
  10. 37430
    “…A profound reduction of aldosterone levels and normalisation of rhythmicity was seen post adrenalectomy. Applying a Random Forest classifier in a subgroup of 20 PA patients, 82% specificity and 85% sensibility were achieved to discriminate PA from HS. …”
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    OR02-03 Twenty-four-hour Tissue Microdialysis Of Aldosterone And Metabolites In Primary Aldosteronism
  11. 37431
    “…Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. …”
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    Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics
  12. 37432
    “…Conversely, spending time in natural environments such as parks or forests, or even viewing nature-themed images in a lab setting, is associated with lower levels of perceived stress and is hypothesized to be a strong stress “buffer,” reducing perceived stress even after leaving the natural setting. …”
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    Ecological Momentary Assessment of Midlife Adults’ Daily Stress: Protocol for the Stress Reports in Variable Environments (STRIVE) App Study
  13. 37433
    “…Outcome measures were prevalence of P. aeruginosa and MRSA in the overall clinically defined cohort and among microbiologically confirmed cases and predictors of P. aeruginosa or MRSA isolation, as determined by univariate logistic regression, bootstrapped least absolute shrinkage and selection operator, and random forest analyses. Additionally, we describe the iterative process used and challenges encountered in carrying out the new ATS/IDSA guideline recommendations. …”
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    Methicillin‐resistant Staphylococcus aureus and Pseudomonas aeruginosa community acquired pneumonia: Prevalence and locally derived risk factors in a single hospital system
  14. 37434
    “…Six approaches—logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine—were implemented in this study. …”
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    A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study
  15. 37435
    “…Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. …”
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    Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI
  16. 37436
    “…Using the preselected 46 clinical and laboratory variables, the area under the receiver operating characteristic curve of CatBoost classifier, random forest classifier, and regularized logistic regression classifier models were 0.860 (95% CI 0.852-0.868), 0.855 (95% CI 0.848-0.863), and 0.823 (95% CI 0.813-0.832), respectively. …”
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    Machine Learning Algorithms Predict Successful Weaning From Mechanical Ventilation Before Intubation: Retrospective Analysis From the Medical Information Mart for Intensive Care IV Database
  17. 37437
    “…The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). …”
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    Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study
  18. 37438
    “…ML models based on baseline respiratory (oropharynx) microbiota profiles exhibited the ability to predict outcomes (survival and death, Random Forest, AUC = 0.847, Sensitivity = 0.833, Specificity = 0.750) after SARS-CoV-2 infection. …”
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    Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis
  19. 37439
    “…The candidate m(6)A regulators were screened using random forest (RF) model to predict the risk of hepatic IRI. …”
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    Key m(6)A regulators mediated methylation modification pattern and immune infiltration characterization in hepatic ischemia-reperfusion injury
  20. 37440
    por Crous, P.W., Wingfield, M.J., Guarro, J., Cheewangkoon, R., van der Bank, M., Swart, W.J., Stchigel, A.M., Cano-Lira, J.F., Roux, J., Madrid, H., Damm, U., Wood, A.R., Shuttleworth, L.A., Hodges, C.S., Munster, M., de Jesús Yáñez-Morales, M., Zúñiga-Estrada, L., Cruywagen, E.M., de Hoog, G.S., Silvera, C., Najafzadeh, J., Davison, E.M., Davison, P.J.N., Barrett, M.D., Barrett, R.L., Manamgoda, D.S., Minnis, A.M., Kleczewski, N.M., Flory, S.L., Castlebury, L.A., Clay, K., Hyde, K.D., Maússe-Sitoe, S.N.D., Chen, Shuaifei, Lechat, C., Hairaud, M., Lesage-Meessen, L., Pawłowska, J., Wilk, M., Śliwińska-Wyrzychowska, A., Mętrak, M., Wrzosek, M., Pavlic-Zupanc, D., Maleme, H.M., Slippers, B., Mac Cormack, W.P., Archuby, D.I., Grünwald, N.J., Tellería, M.T., Dueñas, M., Martín, M.P., Marincowitz, S., de Beer, Z.W., Perez, C.A., Gené, J., Marin-Felix, Y., Groenewald, J.Z.
    Publicado 2013
    “…Species from Europe include Auxarthron longisporum from forest soil (Portugal), Discosia pseudoartocreas from Tilia (Austria), Paraconiothyrium polonense and P. lycopodinum from Lycopodium (Poland) and Stachybotrys oleronensis from Iris (France). …”
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    Fungal Planet description sheets: 154–213
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