Elbers, Paul

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Bastard, P., Rosen, L. B., Zhang, Q., Michailidis, E., Hoffmann, H.-H., Zhang, Y., Dorgham, K., Philippot, Q., Rosain, J., Béziat, V., et al. (2020) · Science

interferon and immune responsesSARS-CoV-2 and COVID-19 Research

Cited by 2,662

Inborn errors of type I IFN immunity in patients with life-threatening COVID-19

Zhang, Q., Bastard, P., Liu, Z., Le Pen, J., Moncada-Velez, M., Chen, J., Ogishi, M., Sabli, I. K. D., Hodeib, S., Korol, C., et al. (2020) · Science

interferon and immune responsesSARS-CoV-2 and COVID-19 Research

Cited by 2,229

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., et al. (2020) · Intensive Care Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 755

Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity

Larsen, M. D., de Graaf, E. L., Sonneveld, M. E., Plomp, H. R., Nouta, J., Hoepel, W., Chen, H.-J., Linty, F., Visser, R., Brinkhaus, M., et al. (2021) · Science

Monoclonal and Polyclonal Antibodies ResearchGlycosylation and Glycoproteins Research

Cited by 408

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*

Thoral, P. J., Peppink, J. M., Driessen, R. H., Sijbrands, E. J. G., Kompanje, E. J. O., Kaplan, L., Bailey, H., Kesecioglu, J., Cecconi, M., Churpek, M., et al. (2021) · Critical Care Medicine

Privacy-Preserving Technologies in DataElectronic Health Records Systems

Cited by 257

Use of artificial intelligence in critical care: opportunities and obstacles

Pinsky, M. R., Bedoya, A., Bihorac, A., Celi, L., Churpek, M., Economou-Zavlanos, N. J., Elbers, P., Saria, S., Liu, V., Lyons, P. G., et al. (2024) · Critical Care

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 160

Ventilator-derived carbon dioxide production to assess energy expenditure in critically ill patients: proof of concept

Stapel, S. N., de Grooth, H.-J. S., Alimohamad, H., Elbers, P. W. G., Girbes, A. R. J., Weijs, P. J. M., & Oudemans-van Straaten, H. M. (2015) · Critical Care

Clinical Nutrition and GastroenterologyHemodynamic Monitoring and Therapy

Cited by 118

Systematic Review and Comparison of Publicly Available ICU Data Sets—A Decision Guide for Clinicians and Data Scientists

Sauer, C. M., Dam, T. A., Celi, L. A., Faltys, M., de la Hoz, M. A. A., Adhikari, L., Ziesemer, K. A., Girbes, A., Thoral, P. J., & Elbers, P. (2022) · Critical Care Medicine

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 104

Machine learning in intensive care medicine: ready for take-off?

Collaborators, R. D. R. N., Fleuren, L. M., Thoral, P., Shillan, D., Ercole, A., & Elbers, P. W. G. (2020) · Intensive Care Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 88

Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.

Cecconi, M., Greco, M., Shickel, B., Angus, D. C., Bailey, H., Bignami, E., Calandra, T., Celi, L. A., Einav, S., Elbers, P., et al. (2025) · Critical Care

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 80

Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis

Roggeveen, L., el Hassouni, A., Ahrendt, J., Guo, T., Fleuren, L., Thoral, P., Girbes, A. R., Hoogendoorn, M., & Elbers, P. W. (2021) · Artificial Intelligence in Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 41

Large language models in critical care

Biesheuvel, L. A., Workum, J. D., Reuland, M., van Genderen, M. E., Thoral, P., Dongelmans, D., & Elbers, P. (2025) · Journal of Intensive Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 36

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Thoral, P. J., Fornasa, M., de Bruin, D. P., Tonutti, M., Hovenkamp, H., Driessen, R. H., Girbes, A. R. J., Hoogendoorn, M., & Elbers, P. W. G. (2021) · Critical Care Explorations

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 34

Clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient: A cross-sectional study.

Fleuren, L. M., Roggeveen, L. F., Guo, T., Waldauf, P., van der Voort, P. H. J., Bosman, R. J., Swart, E. L., Girbes, A. R. J., & Elbers, P. W. G. (2019) · Critical Care

Antibiotics Pharmacokinetics and EfficacyAntibiotic Use and Resistance

Cited by 32

Leveraging the power of routinely collected ICU data

Lijović, L., & Elbers, P. (2025) · Intensive Care Medicine

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 10

Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML

van de Water, R., Schmidt, H., Elbers, P., Thoral, P., Arnrich, B., & Rockenschaub, P. (2023) · arXiv

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 9