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  1. 36981
    “…The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. …”
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    CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study
  2. 36982
    “…In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). …”
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    A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography
  3. 36983
    “…Correlated features (ρ > 0.7) were removed, and the best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). …”
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    Unsuspected Involvement of Spinal Cord in Alzheimer Disease
  4. 36984
    “…In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. …”
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    Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study
  5. 36985
    “…Data were analyzed using (STATA)™ version 14.1 software, and the pooled prevalence with 95% confidence intervals (CI) were presented using tables and forest plots. The presence of statistical heterogeneity within the included studies was evaluated using I-squared statistic. …”
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    Level of self-care practice among diabetic patients in Ethiopia: a systematic review and meta-analysis
  6. 36986
    “…The quality of studies was assessed using the STROBE tool (von Elm et al., 1) Individual study data was analyzed using odds ratios and 95% confidence intervals as a measure of association between exposure (co-infection), patient outcome and results summarised using forest plots and tables RESULTS: Nineteen (19) studies from all over the world were identified and included in the review. …”
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    Single and multiple respiratory virus infections and severity of respiratory disease: A systematic review
  7. 36987
    “…Influence of frequency and power density in the range of soft and hard cavitation on the inactivation of transfusion-relevant model viruses for Hepatitis-(BVDV = bovine diarrhea virus), for Herpes-(SFV = Semliki Forest virus, PRV = pseudorabies virus) and Parvovirus B19 (PPV = porcine parvovirus) were examined. …”
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    Novel method for reduction of virus load in blood plasma by sonication
  8. 36988
    “…The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation. …”
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    Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study
  9. 36989
    “…METHODS: Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. …”
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    Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure
  10. 36990
    “…BACKGROUND: This study evaluated the effectiveness of improved housing on indoor residual mosquito density and exposure to infected Anophelines in Minkoameyos, a rural community in southern forested Cameroon. METHODS: Following the identification of housing factors affecting malaria prevalence in 2013, 218 houses were improved by screening the doors and windows, installing plywood ceilings on open eaves and closing holes on walls and doors. …”
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    The effect of improved housing on indoor mosquito density and exposure to malaria in the rural community of Minkoameyos, Centre Region of Cameroon
  11. 36991
    “…Next, calendar data form the first half of the intervention were retained and summary functions used to create 18 predictors for random forest machine learning models, the classification accuracies of which were ultimately estimated using nested cross-validation. …”
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    Using alcohol consumption diary data from an internet intervention for outcome and predictive modeling: a validation and machine learning study
  12. 36992
    “…Two preliminary prognostic models predictive of LN metastasis were built by random forest with differentially methylated markers shared by plasma and tissue samples and markers present either in plasma or tissue samples respectively. …”
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    DNA methylation markers that correlate with occult lymph node metastases of non-small cell lung cancer and a preliminary prediction model
  13. 36993
    “…METHODS: Assessments were conducted on random samples (n = 440) of LLINs from the eleven districts representing four ecological zones: Terai plain region (Kailali and Kanchanpur districts), outer Terai fluvial ecosystem (Surkhet, Dang, and Rupandhei districts), inner Terai forest ecosystem (Mahhothari, Dhanusa, and Illam districts), and Hills and river valley (Kavrepalanchock and Sindhupalchok districts). …”
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    The durability of long-lasting insecticidal nets distributed to the households between 2009 and 2013 in Nepal
  14. 36994
    “…We observed statistically significant modification of the efficacy of RTS,S/AS01 by forest cover, suggesting that initial vaccine efficacy and the importance of the fourth dose varies based on ecological context. …”
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    Environmental modifiers of RTS,S/AS01 malaria vaccine efficacy in Lilongwe, Malawi
  15. 36995
    “…We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). …”
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    Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population
  16. 36996
    “…In addition, plots from forest, farmland and lake land that were far from GP and largely undisturbed were also investigated as more extreme contrasts (CK-far). …”
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    Excess sulfur and Fe elements drive changes in soil and vegetation at abandoned coal gangues, Guizhou China
  17. 36997
    “…The incidence rates in developed areas of animal husbandry (Horqin Youyi Qianqi [161.2/100 000] and Horqin Youyi Zhongqi [112.1/100 000]) were significantly higher than those in forest areas (Arxan [19.2/100 000]) (χ(2) = 32.561, P < 0.001). …”
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    Epidemiologically characteristics of human brucellosis and antimicrobial susceptibility pattern of Brucella melitensis in Hinggan League of the Inner Mongolia Autonomous Region, China
  18. 36998
    “…Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. …”
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    Predicting EGFR mutation subtypes in lung adenocarcinoma using (18)F-FDG PET/CT radiomic features
  19. 36999
    “…Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. …”
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    Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study
  20. 37000
    “…The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). …”
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    Prediction of the development of acute kidney injury following cardiac surgery by machine learning
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