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Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.

J. Verhaeghe, T. Corte, C. Sauer, T. Hendriks, O. Thijssens, F. Ongenae, P. Elbers, J. Waele, and S. Hoecke. Int. J. Medical Informatics, (July 2023)

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Designing a Pharmacokinetic Machine Learning Model for Optimizing Beta-Lactam Antimicrobial Dosing in Critically Ill Patients.J. Verhaeghe, T. Corte, J. Waele, F. Ongenae, and S. Hoecke. ICMHI, page 311-317. ACM, (2024)Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.J. Verhaeghe, T. Corte, C. Sauer, T. Hendriks, O. Thijssens, F. Ongenae, P. Elbers, J. Waele, and S. Hoecke. Int. J. Medical Informatics, (July 2023)Conformal Prediction for Dose-Response Models with Continuous Treatments.J. Verhaeghe, J. Jonkers, and S. Hoecke. CoRR, (2024)Powershap: A Power-Full Shapley Feature Selection Method.J. Verhaeghe, M. Donckt, F. Ongenae, and S. Hoecke. ECML/PKDD (1), volume 13713 of Lecture Notes in Computer Science, page 71-87. Springer, (2022)

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Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.

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Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.