Sedative medications are routinely administered to provide comfort and facilitate clinical care in critically ill ICU patients. Prior work shows that brain monitoring using electroencephalography (EEG) to track sedation levels may help medical personnel to optimize drug dosing and avoid the adverse effects of oversedation and undersedation. However, the performance of sedation monitoring methods proposed to date deal poorly with individual variability across patients, leading to inconsistent performance. To address this challenge we develop an online learning approach based on Adaptive Regularization of Weight Vectors (AROW). Our approach adaptively updates a sedation level prediction algorithm under a continuously evolving data distribution. The prediction model is gradually calibrated for individual patients in response to EEG observations and routine clinical assessments over time. The evaluations are performed on a population of 172 sedated ICU patients whose sedation levels were assessed using the Richmond Agitation-Sedation Scale (scores between −5 = comatose and 0 = awake). The proposed adaptive model achieves better performance than the same model without adaptation (average accuracies with tolerance of one level difference: 68.76% vs. 61.10%). Moreover, our approach is shown to be robust to sudden changes caused by label noise. Medication administrations have different effects on model performance. We find that the model performs best in patients receiving only propofol, compared to patients receiving no sedation or multiple simultaneous sedative medications.

中文翻译：

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在线学习ICU患者脑电图自适应镇静监测

镇静药物通常用于提供舒适感并促进重症 ICU 患者的临床护理。先前的工作表明，使用脑电图 (EEG) 进行大脑监测以跟踪镇静水平可能有助于医务人员优化药物剂量并避免过度镇静和镇静不足的不良影响。然而，迄今为止提出的镇静监测方法的性能不能很好地处理患者的个体差异，导致性能不一致。为了应对这一挑战，我们开发了一种基于权重向量自适应正则化 (AROW) 的在线学习方法。我们的方法在不断变化的数据分布下自适应地更新镇静水平预测算法。随着时间的推移，响应 EEG 观察和常规临床评估，针对个体患者逐渐校准预测模型。评估是对 172 名镇静的 ICU 患者进行的，这些患者的镇静水平是使用 Richmond 躁动镇静量表评估的（分数介于 -5 = 昏迷和 0 = 清醒之间）。所提出的自适应模型比没有自适应的相同模型获得了更好的性能（具有一级差异容差的平均准确度：68.76% vs. 61.10%）。此外，我们的方法被证明对标签噪声引起的突然变化具有鲁棒性。药物管理对模型性能有不同的影响。我们发现，与未接受镇静或同时服用多种镇静药物的患者相比，该模型在仅接受丙泊酚的患者中表现最佳。