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Results: 14
Number of items: 14
  • Open Access
    de Hond, A. A. H., Kant, I. M. J., Fornasa, M., Cinà, G., Elbers, P. W. G., Thoral, P. J., Arbous, M. S., & Steyerberg, E. W. (2023). Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model. Critical care medicine, 51(2), 291-300. https://doi.org/10.1097/CCM.0000000000005758
  • Open Access
    Dam, T. A., Hoogendoorn, M., Elbers, P. W. G., & Dutch ICU Data Sharing Against COVID-19 Collaborators (2022). Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning. Annals of intensive care, 12, Article 99. https://doi.org/10.1186/s13613-022-01070-0
  • de Vos, J., Visser, L. A., de Beer, A. A., Fornasa, M., Thoral, P. J., Elbers, P. W. G., & Cinà, G. (2022). The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge. Value in Health, 25(3), 359-367. https://doi.org/10.1016/j.jval.2021.06.018
  • Open Access
    Fleuren, L. M., Fornasa, M., Cinà, G., Hoogendoorn, M., Elbers, P. W. G., & Dutch ICU Data Sharing Against COVID-19 Collaborators (2021). Predictors for extubation failure in COVID-19 patients using a machine learning approach. Critical Care, 25, Article 448. https://doi.org/10.1186/s13054-021-03864-3
  • Open Access
    CTA Consortium (2019). Science with the Cherenkov Telescope Array. World Scientific. https://doi.org/10.1142/10986
  • Troster, T., Camera, S., Fornasa, M., Regis, M., van Waerbeke, L., Harnois-Déraps, J., Ando, S., Bilicki, M., Erben, T., Fornengo, N., Heymans, C., Hildebrandt, H., Hoekstra, H., Kuijken, K., & Viola, M. (2017). Cross-correlation of weak lensing and gamma rays: implications for the nature of dark matter. Monthly Notices of the Royal Astronomical Society, 467(3), 2706-2722. https://doi.org/10.1093/mnras/stx365
  • Open Access
    Ando, S., Fornasa, M., Fornengo, N., Regis, M., & Zechlin, H.-S. (2017). Astrophysical interpretation of the anisotropies in the unresolved gamma-ray background. Physical Review D. Particles, Fields, Gravitation, and Cosmology, 95(12), Article 123006. https://doi.org/10.1103/PhysRevD.95.123006
  • Open Access
    Ando, S., Feyereisen, M. R., & Fornasa, M. (2017). How bright can the brightest neutrino source be? Physical Review D. Particles, Fields, Gravitation, and Cosmology, 95(10), Article 103003. https://doi.org/10.1103/PhysRevD.95.103003
  • Open Access
    Cerdeño, D. G., Fornasa, M., Green, A. M., & Peiró, M. (2016). How to calculate dark matter direct detection exclusion limits that are consistent with gamma rays from annihilation in the Milky Way halo. Physical Review D - Particles, Fields, Gravitation and Cosmology, 94(4), Article 043516. https://doi.org/10.1103/PhysRevD.94.043516
  • Open Access
    Camera, S., Fornasa, M., Fornengo, N., & Regis, M. (2016). Detecting particle dark matter signatures by cross-correlating γ-ray anisotropies with weak lensing. Journal of Physics: Conference Series, 718(3), Article 032003 . https://doi.org/10.1088/1742-6596/718/3/032003
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