Publications by authors named "D W Donker"

Article Synopsis
  • ECMO (Extracorporeal Membrane Oxygenation) has high complication rates, prompting the need for improved management strategies, which led to the development of the REMAP ECMO platform to investigate effective patient management techniques.* -
  • The REMAP ECMO platform allows for multiple adaptive randomized controlled trials, with the first focusing on the effects of early left ventricular unloading via intra-aortic balloon pumping compared to ECMO alone for cardiogenic shock patients in the ICU.* -
  • The primary outcome aims to determine successful weaning from ECMO at 30 days, while secondary outcomes include intervention needs, survival rates, and quality of life, all analyzed using a flexible Bayesian statistical framework.*
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Background: The improvement of controllers of left ventricular assist device (LVAD) technology supporting heart failure (HF) patients has enormous impact, given the high prevalence and mortality of HF in the population. The use of reinforcement learning for control applications in LVAD remains minimally explored. This work introduces a preload-based deep reinforcement learning control for LVAD based on the proximal policy optimization algorithm.

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Rationale: Multiple mechanisms are involved in the pathogenesis of obstructive sleep apnea (OSA). Elevated loop gain is a key target for precision OSA care and may be associated with treatment intolerance when the upper airway is the sole therapeutic target. Morphological or computational estimation of LG is not yet widely available or fully validated - there is a need for improved phenotyping/endotyping of apnea to advance its therapy and prognosis.

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