PDF(Pubmed)
Abstract：
Quantifying pain in patients admitted to intensive care units (ICUs) is challenging due to the increased prevalence of communication barriers in this patient population. Previous research has posited a positive correlation between pain and physical activity in critically ill patients. In this study, we advance this hypothesis by building machine learning classifiers to examine the ability of accelerometer data collected from daily wearables to predict self-reported pain levels experienced by patients in the ICU. We trained multiple Machine Learning (ML) models, including Logistic Regression, CatBoost, and XG-Boost, on statistical features extracted from the accelerometer data combined with previous pain measurements and patient demographics. Following previous studies that showed a change in pain sensitivity in ICU patients at night, we performed the task of pain classification separately for daytime and nighttime pain reports. In the pain versus no-pain classification setting, logistic regression gave the best classifier in daytime (AUC: 0.72, F1-score: 0.72), and CatBoost gave the best classifier at nighttime (AUC: 0.82, F1-score: 0.82). Performance of logistic regression dropped to 0.61 AUC, 0.62 F1-score (mild vs. moderate pain, nighttime), and CatBoost\'s performance was similarly affected with 0.61 AUC, 0.60 F1-score (moderate vs. severe pain, daytime). The inclusion of analgesic information benefited the classification between moderate and severe pain. SHAP analysis was conducted to find the most significant features in each setting. It assigned the highest importance to accelerometer-related features on all evaluated settings but also showed the contribution of the other features such as age and medications in specific contexts. In conclusion, accelerometer data combined with patient demographics and previous pain measurements can be used to screen painful from painless episodes in the ICU and can be combined with analgesic information to provide moderate classification between painful episodes of different severities.
摘要：
由于该患者人群中沟通障碍的患病率增加，因此量化重症监护病房（ICU）患者的疼痛具有挑战性。先前的研究认为，危重患者的疼痛与身体活动之间存在正相关。在这项研究中,我们通过构建机器学习分类器来检验从每日可穿戴设备收集的加速度计数据预测ICU患者自我报告的疼痛水平的能力,从而推进了这一假设.我们训练了多个机器学习(ML)模型，包括Logistic回归，CatBoost,和XG-Boost，从加速度计数据中提取的统计特征，结合以前的疼痛测量和患者人口统计学。根据先前的研究表明，夜间ICU患者的疼痛敏感性发生变化，我们对日间和夜间疼痛报告分别进行了疼痛分类.在疼痛与无痛分类设置中，逻辑回归给出了白天的最佳分类器（AUC：0.72，F1评分：0.72），和CatBoost在夜间给出最好的分类器(AUC:0.82,F1得分:0.82)。逻辑回归的性能下降到0.61AUC，0.62F1评分（轻度vs.中度疼痛,夜间)，和CatBoost的性能同样受到0.61AUC的影响，0.60F1分数（中等与中等剧烈疼痛,白天)。包含镇痛信息有利于中度和重度疼痛之间的分类。进行SHAP分析以找到每种设置中最重要的特征。它在所有评估的设置中对加速度计相关功能赋予了最高的重要性，但也显示了其他功能的贡献，如年龄和药物在特定环境中的贡献。总之,加速度计数据与患者人口统计学和先前的疼痛测量值相结合,可用于从ICU中的无痛发作中筛查疼痛,并可与镇痛信息相结合,以在不同严重程度的疼痛发作之间提供中等程度的分类.
