The demise of early goal‐directed therapy for severe sepsis and septic shock

By P. Marik. 2015

50 citations recorded by the source corpus.

Subfields: Emergency Medicine, Epidemiology, Surgery, Medical & Health AI

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Citations in the corpus

Deconfounding Reinforcement Learning in Observational Settings (2018, background): “…by Raghu et al. (2017a), RL also has advantages over other machine learning algorithms even in the observational setting, especially in two situations: when the optimal treatment strategy is unclear in medical literature (Marik, 2015), and when training examples do not represent optimal behavior.”
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (2018, methodology): “hat the behaviors of doctors are prone to be imperfect. Due to the knowledge gap and limited experiences of doctors, the ground truth of “good” treatment strategy is unclear in the medical literature [21]. To address this issue, we prefer to use RL that is well-suited to infer optimal policies based”
Deep Reinforcement Learning for Sepsis Treatment (2017, methodology): “We prefer RL for sepsis treatment over supervised learning, because the ground truth of a “good” treatment strategy is unclear in medical literature [7].”
Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017, methodology): “The action space was restricted to these two interventions as both drugs are extremely important in the management of septic patients, but there is no agreement on when, and how much, of each drug to give (Marik, 2015).”
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning (2018, unknown): “pk = sigmoid(w · x+ b) and pd = 1− pk (5)”