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  1. 36621
    “…We compared the performance of the MTCNN models against single-task CNN models and 2 traditional machine learning approaches, namely support vector machine (SVM) and random forest classifier (RFC). RESULTS: MTCNNs offered superior performance across all 5 tasks in terms of classification accuracy as compared with the other machine learning models. …”
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    Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks
  2. 36622
    “…We counted the APRI, NLR, PLR, and LMR before treatment and calculated their cut-off values for predicting overall survival (OS) and progression-free survival (PFS) by receiver operating characteristic (ROC) analysis. The random forest model combined with least absolute shrinkage and selection operator (LASSO) regression model for OS and PFS were used to screen potentially prognostic factors from serum inflammatory markers, demographic data, and clinical characteristics. …”
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    Development and Assessment of Novel Predictive Nomograms Based on APRI for Hepatitis B Virus-associated Small Solitary Hepatocellular Carcinoma with Stereotactic Body Radiotherapy
  3. 36623
    “…We test hypotheses regarding putative forest refugia and expansion events associated with past climatic changes in the wood frog Batrachyla leptopus distributed along ∼1,000 km of length including glaciated and non-glaciated areas in southwestern Patagonia. …”
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    Phylogeographic analysis and species distribution modelling of the wood frog Batrachyla leptopus (Batrachylidae) reveal interglacial diversification in south western Patagonia
  4. 36624
    “…For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. …”
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    CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence
  5. 36625
    “…The same integration process accelerated about 40 ka ago, when forested areas retreated in the Lebanese Mountains. …”
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    Al-Ansab and the Dead Sea: Mid-MIS 3 archaeology and environment of the early Ahmarian population of the Levantine corridor
  6. 36626
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    COVID-19 infection in kidney transplant recipients at the epicenter of pandemics
  7. 36627
    por Baek, Bin, Lee, Hyunju
    Publicado 2020
    “…When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). …”
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    Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data
  8. 36628
    “…The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87–0.89) and 0.89 (95% CI = 0.88–0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). …”
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    Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data
  9. 36629
    “…Multivariate logistic regression analysis and the forest plot of hazard ratio (HR) was made to assess the association between potential prognostic factors, including surgery and different surgical methods, and survival in elderly patients. …”
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    Can Elderly Patients With Pancreatic Cancer Gain Survival Advantages Through More Radical Surgeries? A SEER-Based Analysis
  10. 36630
    “…Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. …”
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    Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank
  11. 36631
    por Spiegel, Jacob, Senderowitz, Hanoch
    Publicado 2020
    “…Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. …”
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    Evaluation of QSAR Equations for Virtual Screening
  12. 36632
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    Handwashing and Detergent Treatment Greatly Reduce SARS-CoV-2 Viral Load on Halloween Candy Handled by COVID-19 Patients
  13. 36633
    “…We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. …”
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    Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer’s Disease: An Exploratory Study
  14. 36634
    “…Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. …”
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    Estimating salt content of vegetated soil at different depths with Sentinel-2 data
  15. 36635
    “…Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. …”
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    Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI
  16. 36636
    “…METHODS: Clinical samples from patients with suspected infection during calendar year 2018 and 2019 were processed in the microbiology lab of Wake Forest Baptist Medical Center. After incubation, SM colonies were identified by MALDI-TOF system. …”
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    1600. Closing the gap on moxifloxacin breakpoints for Stenotrophomonas maltophilia
  17. 36637
    “…Additional clinical data such as comorbid disease and baseline laboratory parameters were extracted through electronic query. A random forest model imputed missing data. A propensity score for NSAID use was developed via logistic regression and included gender, back pain, baseline serum creatinine, osteoarthritis, rheumatoid arthritis, serum albumin, and use of anticoagulant or antiplatelet medications. …”
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    791. Evaluation of NSAID Exposure as a Risk Factor for Clostridium difficile infection: A Propensity-Score-Matched Case-Control Study
  18. 36638
    por Hofman, P., Ilié, M., Chamorey, E., Brest, P., Schiappa, R., Nakache, V., Antoine, M., Barberis, M., Begueret, H., Bibeau, F., Bonnetaud, C., Boström, P., Brousset, P., Bubendorf, L., Carvalho, L., Cathomas, G., Cazes, A., Chalabreysse, L., Chenard, M.-P., Copin, M.-C., Côté, J.-F., Damotte, D., de Leval, L., Delongova, P., Thomas de Montpreville, V., de Muret, A., Dema, A., Dietmaier, W., Evert, M., Fabre, A., Forest, F., Foulet, A., Garcia, S., Garcia-Martos, M., Gibault, L., Gorkiewicz, G., Jonigk, D., Gosney, J., Hofman, A., Kern, I., Kerr, K., Kossai, M., Kriegsmann, M., Lassalle, S., Long-Mira, E., Lupo, A., Mamilos, A., Matěj, R., Meilleroux, J., Ortiz-Villalón, C., Panico, L., Panizo, A., Papotti, M., Pauwels, P., Pelosi, G., Penault-Llorca, F., Pop, O., Poté, N., Cajal, S.R.Y., Sabourin, J.-C., Salmon, I., Sajin, M., Savic-Prince, S., Schildhaus, H.-U., Schirmacher, P., Serre, I., Shaw, E., Sizaret, D., Stenzinger, A., Stojsic, J., Thunnissen, E., Timens, W., Troncone, G., Werlein, C., Wolff, H., Berthet, J.-P., Benzaquen, J., Marquette, C.-H., Hofman, V., Calabrese, F.
    Publicado 2020
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    Clinical and molecular practice of European thoracic pathology laboratories during the COVID-19 pandemic. The past and the near future
  19. 36639
    “…Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. …”
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    Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis
  20. 36640
    “…Ranched deer selected bottomland mixed forest and areas closer to tertiary roads, supplementary food sources, and permanent water. …”
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    Resource Selection by Wild and Ranched White-Tailed Deer (Odocoileus virginianus) during the Epizootic Hemorrhagic Disease Virus (EHDV) Transmission Season in Florida
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