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Results: 20
Number of items: 20
  • Open Access
    Cinà, G., Röber, T. E., Goedhart, R., & Birbil, Ş. İ. (2025). Why we do need explainable AI for healthcare. Diagnostic and Prognostic Research, 9, Article 24. https://doi.org/10.1186/s41512-025-00209-4
  • Open Access
    van Geloven, N., Keogh, R. H., van Amsterdam, W., Cinà, G., Krijthe, J. H., Peek, N., Luijken, K., Magliacane, S., Morzywołek, P., van Ommen, T., Putter, H., Sperrin, M., Wang, J., Weir, D. L., & Didelez, V. (2025). The Risks of Risk Assessment: Causal Blind Spots When Using Prediction Models for Treatment Decisions. Annals of Internal Medicine, 178(9), 1326-1333. https://doi.org/10.7326/ANNALS-24-00279
  • Open Access
    Luijken, K., Morzywołek, P., van Amsterdam, W., Cinà, G., Hoogland, J., Keogh, R., Krijthe, J. H., Magliacane, S., van Ommen, T., Peek, N., Putter, H., van Smeden, M., Sperrin, M., Wang, J., Weir, D. L., Didelez, V., & van Geloven, N. (2024). Risk‐Based Decision Making: Estimands for Sequential Prediction Under Interventions. Biometrical Journal, 66(8), Article e70011. https://doi.org/10.1002/bimj.70011
  • Zadorozhny, K., Thoral, P., Elbers, P., & Cinà, G. (2023). Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation. In A. Shaban-Nejad, M. Michalowski, & S. Bianco (Eds.), Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence (pp. 137-153). (Studies in Computational Intelligence; Vol. 1060). Springer. https://doi.org/10.1007/978-3-031-14771-5_10
  • 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
    van der Meijden, S. L., de Hond, A. A. H., Thoral, P. J., Steyerberg, E. W., Kant, I. M. J., Cinà, G., & Arbous, M. S. (2023). Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study. JMIR Human Factors, 10, Article e39114. https://doi.org/10.2196/39114
  • Open Access
    Cina, G., Röber, T. E., Goedhart, R., & Birbil, S. I. (2023). Semantic match: Debugging feature attribution methods in XAI for healthcare. Proceedings of Machine Learning Research, 209, 182-191. https://proceedings.mlr.press/v209/cina23a.html
  • 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
    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
  • Open Access
    Cina, G., Röber, T., Goedhart, R., & Birbil, I. (2022). Why we do need Explainable AI for Healthcare. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2206.15363
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