Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.
Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.
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DOI：
10.3390/s22103866
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发表时间：
2022-05-19
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--
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脓毒症与高死亡率相关，尤其是在中低收入国家 (LMIC)。由于缺乏护理人员和床边监护仪的成本高昂，中低收入国家脓毒症的重症监护管理面临挑战。医疗保健领域可穿戴传感器技术和机器学习 (ML) 模型的最新进展有望提供与自动化决策系统集成的数字监测新方法，以降低败血症的死亡风险。在本研究中，首先，我们的目的是评估在入院患者脓毒症护理管理中使用可穿戴传感器代替传统床边监护仪的可行性，其次，引入自动预测模型来预测脓毒症患者的死亡率。为此，我们对50名脓毒症患者入住越南热带病医院后近24小时进行了持续监测。然后，我们使用可穿戴传感器的心率变异性 (HRV) 信号和床边监护仪的生命体征，比较了最先进的 ML 模型在脓毒症死亡率预测任务中的性能和可解释性。我们的结果表明，在死亡率预测任务中，所有基于可穿戴数据训练的 ML 模型都优于基于从床边监视器收集的数据训练的 ML 模型，并且使用 HRV 和循环神经网络的时变特征实现了最高性能（精确召回曲线下面积 = 0.83）。我们的结果表明，自动化机器学习预测模型与可穿戴技术的集成非常适合帮助管理中低收入国家脓毒症患者的临床医生降低脓毒症的死亡风险。
Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.
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