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Maud A S Weerink

Showing results (1-10 of 7) with videos related to

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Sleep|August 30, 2020
Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learningSowmya M Ramaswamy, Maud A S Weerink, Michel M R F Struys, et al.
Anesthesia and Analgesia|April 15, 2020
Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing ApproachSunil Belur Nagaraj, Sowmya M Ramaswamy, Maud A S Weerink, et al.
Clinical Pharmacokinetics|January 21, 2017
Clinical Pharmacokinetics and Pharmacodynamics of DexmedetomidineMaud A S Weerink, Michel M R F Struys, Laura N Hannivoort, et al.
British Journal of Anaesthesia|July 22, 2019
Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteersSowmya M Ramaswamy, Merel H Kuizenga, Maud A S Weerink, et al.
Plos One|July 2, 2024
Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogramSowmya M Ramaswamy, Merel H Kuizenga, Maud A S Weerink, et al.
Anesthesiology|December 1, 2021
Dexmedetomidine Clearance Decreases with Increasing Drug Exposure: Implications for Current Dosing Regimens and Target-controlled Infusion Models Assuming Linear PharmacokineticsRicardo Alvarez-Jimenez, Maud A S Weerink, Laura N Hannivoort, et al.
Anesthesiology|August 20, 2019
Pharmacodynamic Interaction of Remifentanil and Dexmedetomidine on Depth of Sedation and Tolerance of LaryngoscopyMaud A S Weerink, Clemens R M Barends, Ernesto R R Muskiet, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Sleep|August 30, 2020
Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learningSowmya M Ramaswamy, Maud A S Weerink, Michel M R F Struys, et al.
Anesthesia and Analgesia|April 15, 2020
Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing ApproachSunil Belur Nagaraj, Sowmya M Ramaswamy, Maud A S Weerink, et al.
Clinical Pharmacokinetics|January 21, 2017
Clinical Pharmacokinetics and Pharmacodynamics of DexmedetomidineMaud A S Weerink, Michel M R F Struys, Laura N Hannivoort, et al.
British Journal of Anaesthesia|July 22, 2019
Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteersSowmya M Ramaswamy, Merel H Kuizenga, Maud A S Weerink, et al.
Plos One|July 2, 2024
Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogramSowmya M Ramaswamy, Merel H Kuizenga, Maud A S Weerink, et al.
Anesthesiology|December 1, 2021
Dexmedetomidine Clearance Decreases with Increasing Drug Exposure: Implications for Current Dosing Regimens and Target-controlled Infusion Models Assuming Linear PharmacokineticsRicardo Alvarez-Jimenez, Maud A S Weerink, Laura N Hannivoort, et al.
Anesthesiology|August 20, 2019
Pharmacodynamic Interaction of Remifentanil and Dexmedetomidine on Depth of Sedation and Tolerance of LaryngoscopyMaud A S Weerink, Clemens R M Barends, Ernesto R R Muskiet, et al.
Pageof 1