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Celine Vens

Showing results (11-20 of 36) with videos related to

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Artificial Intelligence in Medicine|March 29, 2024
Predicting time-to-intubation after critical care admission using machine learning and cured fraction informationMichela Venturini, Ingrid Van Keilegom, Wouter De Corte, et al.
Computers in Biology and Medicine|December 18, 2022
Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classificationMiguel Romero, Felipe Kenji Nakano, Jorge Finke, et al.
Scientific Reports|June 18, 2023
Predicting outcomes of acute kidney injury in critically ill patients using machine learningFateme Nateghi Haredasht, Liesbeth Viaene, Hans Pottel, et al.
Cytometry. Part a : the Journal of the International Society for Analytical Cytology|August 6, 2015
FloReMi: Flow density survival regression using minimal feature redundancySofie Van Gassen, Celine Vens, Tom Dhaene, et al.
BMC Bioinformatics|January 5, 2010
Predicting gene function using hierarchical multi-label decision tree ensemblesLeander Schietgat, Celine Vens, Jan Struyf, et al.
Therapie|October 30, 2025
Artificial intelligence for precision medicineMarie-Elise Martel, Adan José-Garcia, Celine Vens, et al.
Intensive & Critical Care Nursing|April 12, 2026
Wearable and wireless continuous monitoring for early detection of clinical deterioration in high-risk inpatients: a scoping reviewLouis Van Slambrouck, Emilie Long, Lucy Van Kleunen, et al.
Journal of Clinical Medicine|December 23, 2022
Comparison between Cystatin C- and Creatinine-Based Estimated Glomerular Filtration Rate in the Follow-Up of Patients Recovering from a Stage-3 AKI in ICUFateme Nateghi Haredasht, Liesbeth Viaene, Celine Vens, et al.
BMC Nephrology|May 10, 2023
Validated risk prediction models for outcomes of acute kidney injury: a systematic reviewFateme Nateghi Haredasht, Laban Vanhoutte, Celine Vens, et al.
European Heart Journal. Cardiovascular Imaging|June 27, 2020
Applying machine learning to detect early stages of cardiac remodelling and dysfunctionFrantišek Sabovčik, Nicholas Cauwenberghs, Dmitry Kouznetsov, et al.
Pageof 4

Showing results (11-20 of 36) with videos related to

Sort By:
Pageof 4
Artificial Intelligence in Medicine|March 29, 2024
Predicting time-to-intubation after critical care admission using machine learning and cured fraction informationMichela Venturini, Ingrid Van Keilegom, Wouter De Corte, et al.
Computers in Biology and Medicine|December 18, 2022
Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classificationMiguel Romero, Felipe Kenji Nakano, Jorge Finke, et al.
Scientific Reports|June 18, 2023
Predicting outcomes of acute kidney injury in critically ill patients using machine learningFateme Nateghi Haredasht, Liesbeth Viaene, Hans Pottel, et al.
Cytometry. Part a : the Journal of the International Society for Analytical Cytology|August 6, 2015
FloReMi: Flow density survival regression using minimal feature redundancySofie Van Gassen, Celine Vens, Tom Dhaene, et al.
BMC Bioinformatics|January 5, 2010
Predicting gene function using hierarchical multi-label decision tree ensemblesLeander Schietgat, Celine Vens, Jan Struyf, et al.
Therapie|October 30, 2025
Artificial intelligence for precision medicineMarie-Elise Martel, Adan José-Garcia, Celine Vens, et al.
Intensive & Critical Care Nursing|April 12, 2026
Wearable and wireless continuous monitoring for early detection of clinical deterioration in high-risk inpatients: a scoping reviewLouis Van Slambrouck, Emilie Long, Lucy Van Kleunen, et al.
Journal of Clinical Medicine|December 23, 2022
Comparison between Cystatin C- and Creatinine-Based Estimated Glomerular Filtration Rate in the Follow-Up of Patients Recovering from a Stage-3 AKI in ICUFateme Nateghi Haredasht, Liesbeth Viaene, Celine Vens, et al.
BMC Nephrology|May 10, 2023
Validated risk prediction models for outcomes of acute kidney injury: a systematic reviewFateme Nateghi Haredasht, Laban Vanhoutte, Celine Vens, et al.
European Heart Journal. Cardiovascular Imaging|June 27, 2020
Applying machine learning to detect early stages of cardiac remodelling and dysfunctionFrantišek Sabovčik, Nicholas Cauwenberghs, Dmitry Kouznetsov, et al.
Pageof 4