强化学习模型在急诊护理中用于脓毒症治疗的可移植性面临的挑战。
Challenges with reinforcement learning model transportability for sepsis treatment in emergency care.
作者信息
Nauka Peter C, Kennedy Jason N, Brant Emily B, Komorowski Matthieu, Pirracchio Romain, Angus Derek C, Seymour Christopher W
机构信息
Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
出版信息
NPJ Digit Med. 2025 Feb 6;8(1):91. doi: 10.1038/s41746-025-01485-6.
Pivotal moments in sepsis care occur in the emergency department (ED), however, and it is unclear whether ED data is adequate to inform reinforcement learning (RL) models. We evaluated the early opportunity for the AI Clinician, a validated ICU-based RL-model, as a use case. Amongst emergency sepsis patients, model parameters were often missing and invariably measured. Current iterations of RL-models trained on ICU data face challenges in emergency sepsis care.
然而，脓毒症治疗的关键阶段发生在急诊科（ED），目前尚不清楚ED数据是否足以用于强化学习（RL）模型。我们评估了基于ICU的经过验证的RL模型——AI临床医生作为一个应用案例的早期应用机会。在急诊脓毒症患者中，模型参数经常缺失且测量结果始终不一致。目前基于ICU数据训练的RL模型在急诊脓毒症治疗中面临挑战。
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