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  1. 36861
    “…We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O(3) at a single monitoring site in Kennewick, WA. …”
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    Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest
  2. 36862
    por Crous, P.W., Osieck, E.R., Jurjević, Ž., Boers, J., van Iperen, A.L., Starink-Willemse, M., Dima, B., Balashov, S., Bulgakov, T.S., Johnston, P.R., Morozova, O.V., Pinruan, U., Sommai, S., Alvarado, P., Decock, C.A., Lebel, T., McMullan-Fisher, S., Moreno, G., Shivas, R.G., Zhao, L., Abdollahzadeh, J., Abrinbana, M., Ageev, D.V., Akhmetova, G., Alexandrova, A.V., Altés, A., Amaral, A.G.G., Angelini, C., Antonín, V., Arenas, F., Asselman, P., Badali, F., Baghela, A., Bañares, A., Barreto, R.W., Baseia, I.G., Bellanger, J.-M., Berraf-Tebbal, A., Biketova, A.Yu., Bukharova, N.V., Burgess, T.I., Cabero, J., Câmara, M.P.S., Cano-Lira, J.F., Ceryngier, P., Chávez, R., Cowan, D.A., de Lima, A.F., Oliveira, R.L., Denman, S., Dang, Q.N., Dovana, F., Duarte, I.G., Eichmeier, A., Erhard, A., Esteve-Raventós, F., Fellin, A., Ferisin, G., Ferreira, R.J., Ferrer, A., Finy, P., Gaya, E., Geering, A.D.W., Gil-Durán, C., Glässnerová, K., Glushakova, A.M., Gramaje, D., Guard, F.E., Guarnizo, A.L., Haelewaters, D., Halling, R.E., Hill, R., Hirooka, Y., Hubka, V., Iliushin, V.A., Ivanova, D.D., Ivanushkina, N.E., Jangsantear, P., Justo, A., Kachalkin, A.V., Kato, S., Khamsuntorn, P., Kirtsideli, I.Y., Knapp, D.G., Kochkina, G.A., Koukol, O., Kovács, G.M., Kruse, J., Kumar, T.K.A., Kušan, I., Læssøe, T., Larsson, E., Lebeuf, R., Levicán, G., Loizides, M., Marinho, P., Luangsa-ard, J.J., Lukina, E.G., Magaña-Dueñas, V., Maggs-Kölling, G., Malysheva, E.F., Malysheva, V.F., Martín, B., Martín, M.P., Matočec, N., McTaggart, A.R., Mehrabi-Koushki, M., Mešić, A., Miller, A.N., Mironova, P., Moreau, P.-A., Morte, A., Müller, K., Nagy, L.G., Nanu, S., Navarro-Ródenas, A., Nel, W.J., Nguyen, T.H., Nóbrega, T.F., Noordeloos, M.E., Olariaga, I., Overton, B.E., Ozerskaya, S.M., Palani, P., Pancorbo, F., Papp, V., Pawłowska, J., Pham, T.Q., Phosri, C., Popov, E.S., Portugal, A., Pošta, A., Reschke, K., Reul, M., Ricci, G.M., Rodríguez, A., Romanowski, J., Ruchikachorn, N., Saar, I., Safi, A., Sakolrak, B., Salzmann, F., Sandoval-Denis, M., Sangwichein, E., Sanhueza, L., Sato, T., Sastoque, A., Senn-Irlet, B., Shibata, A., Siepe, K., Somrithipol, S., Spetik, M., Sridhar, P., Stchigel, A.M., Stuskova, K., Suwannasai, N., Tan, Y.P., Thangavel, R., Tiago, I., Tiwari, S., Tkalčec, Z., Tomashevskaya, M.A., Tonegawa, C., Tran, H.X., Tran, N.T., Trovão, J., Trubitsyn, V.E., Van Wyk, J., Vieira, W.A.S., Vila, J., Visagie, C.M., Vizzini, A., Volobuev, S.V., Vu, D.T., Wangsawat, N., Yaguchi, T., Ercole, E., Ferreira, B.W., de Souza, A.P., Vieira, B.S., Groenewald, J.Z.
    Publicado 2021
    “…Vietnam, Entoloma kovalenkoi on rotten wood, Fusarium chuoi inside seed of Musa itinerans, Micropsalliota albofelina on soil in tropical evergreen mixed forests and Phytophthora docyniae from soil and roots of Docynia indica. …”
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    Fungal Planet description sheets: 1284–1382
  3. 36863
    “…The rest non-pCR cases were served as the test set. Random forest (RF), support vector machine (SVM), and fully connected neural network (FCNN) were applied to establish a 1-dimensional (1D) model based on mRNA data. …”
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    Development and validation of a prognostic model for HER2-low breast cancer to evaluate neoadjuvant therapy
  4. 36864
    “…Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. …”
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    Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study
  5. 36865
    “…These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. …”
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    Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning
  6. 36866
    “…Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. …”
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    Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules
  7. 36867
    “…The selected parks represent two different types: a centrally located park with much infrastructure and open landscapes (Gorky Park) and parks located at the outskirts of the city center with a more forested landscape and little infrastructure (Timiryazevski and Sokolniki parks). …”
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    COVID-19 pandemic changes the recreational use of Moscow parks in space and time: Outcomes from crowd-sourcing and machine learning
  8. 36868
    “…A total of 841 patients who underwent hepatectomy in 10 trials were included in the comparative analysis between low central venous pressure (CVP) and control groups. The forest plots showed a low operative bleeding volume [(mean difference (MD): -409.75 mL, 95% confidence intervals (CI) -616.56 to -202.94, P < 0.001], reduced blood transfusion rate [risk ratio (RR): 0.47, 95% CI 0.34 to 0.65, P < 0.001], shortened operating time (MD: -13.42 min, 95% CI -22.59 to -4.26, P = 0.004), and fewer postoperative complications (RR: 0.76, 95% CI 0.58 to 0.99, P = 0.04) in the low CVP group than in the control group. …”
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    Application of cardiovascular interventions to decrease blood loss during hepatectomy: a systematic review and meta-analysis
  9. 36869
    “…We applied seven ML algorithms: logistic regression, random forest (RF), LASSO, support vector machine, k-Nearest Neighbor, Naive Bayesian Model, Artificial Neural Network. …”
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    Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer
  10. 36870
    “…We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. …”
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    iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
  11. 36871
    “…A total of seven key m6A regulators, including WTAP, ZCH3H13, YTHDC1, FMR1, FTO, RBM15 and YTHDF3, were identified using a random forest classifier. A diagnostic nomogram based on these seven key m6A regulators could effectively distinguish patients with ICM from healthy subjects. …”
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    m6A regulator-mediated RNA methylation modification patterns are involved in the regulation of the immune microenvironment in ischaemic cardiomyopathy
  12. 36872
    “…We applied causal random forest to classify individuals into two environments: E1 and E0. …”
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    Prenatal environmental exposures associated with sex differences in childhood obesity and neurodevelopment
  13. 36873
    “…In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. …”
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    Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region
  14. 36874
    “…The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM(10), PM(2.5)) from January 2020 to September 2021. …”
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    The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK
  15. 36875
    “…Then, the Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) methods were used to predict patients' hospital mortality with traumatic injuries. …”
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    Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms
  16. 36876
    “…The pooled estimate of IL-6 revealed a mean value of 20.92 pg/ml (95% CI = 9.30–32.54 pg/ml, I(2) = 100%, P < 0.01) for long COVID-19 patients. The forest plot showed high levels of IL-6 for long COVID-19 compared with healthy controls (mean difference = 9.75 pg/ml, 95% CI = 5.75–13.75 pg/ml, I(2) = 100%, P < 0.00001) and PASC category (mean difference = 3.32 pg/ml, 95% CI = 0.22–6.42 pg/ml, I(2) = 88%, P = 0.04). …”
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    Increased interleukin-6 is associated with long COVID-19: a systematic review and meta-analysis
  17. 36877
    “…For predictive modeling, we assessed the performance of different machine learning models, including random forests (RFs), support vector machines (SVMs), multilayer perceptron (MLP), and K-nearest neighbor (KNN). …”
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    Objective Prediction of Next-Day’s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study
  18. 36878
    “…It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). …”
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    The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review
  19. 36879
    “…The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects. …”
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    Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels
  20. 36880
    “…METHODS: Six machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Trees (GBT), Extreme Gradient Boosting (XGB), and an ensemble of the five baseline models were developed to predict postoperative clinical outcomes. …”
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    The prognostic value of intraoperative HRV during anesthesia in patients presenting for non-cardiac surgery
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