Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care
Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care
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DOI：
10.1109/bigdata55660.2022.10020963
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发表时间：
2022-12
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影响因子：
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通讯作者：
Ankur Teredesai;Sijin Huang;Tucker Stewart;Juhua Hu;Armaan Thakker;Katherine Stern;G. O’Keefe
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文献类型：
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作者：
Ankur Teredesai;Sijin Huang;Tucker Stewart;Juhua Hu;Armaan Thakker;Katherine Stern;G. O’Keefe
脓毒症是身体对感染的极端炎症反应。它是全球重症监护和ICU中死亡的主要原因之一，导致重症人群约25%的死亡率。早期识别和干预对于降低脓毒症相关死亡率和改善患者预后至关重要，因为严重脓毒症病例可导致器官衰竭和其他危及生命的并发症。脓毒症的诊断在诊断准确性和及时性方面具有挑战性，这是由于在具有不规则时间序列的异质变化的数据源中捕获的模糊症状和个体差异。近年来，已经做出了许多努力，使用机器学习方法进行脓毒症预测。然而，由于输入数据的一致性的限制，仍然存在非常有限的成功的工业规模实施，这通常不符合现实世界的重症监护环境中的不确定时间间隔和大量缺失值的特征。在这项研究中，我们展示了一种创新的方法来实时预测脓毒症的发生，在具有多个时间粒度的异构变量集上，使用灵活的图结构来模拟患者的健康记录。我们的建模任务是使用过去12小时内任何时间窗的观察数据预测入院后48小时内任何时间的未来脓毒症风险。据我们所知，我们提出的方法是有史以来第一个动态的实现，使用图形表示来克服不规则输入特征的问题，并提供连续的风险预测，从而提高模型在临床环境中的兼容性。与之前使用合成数据过滤器报告去识别的策划公共数据集的结果不同，我们的实验和结果由临床专家验证，并且基于大型1级创伤中心的多年真实世界纵向数据。
Sepsis is an extreme inflammatory response of the body to an infection. It is one of the leading causes of death in critical care and ICUs worldwide, resulting in approximately 25% mortality in critically ill populations. Early identification and intervention are crucial to reducing sepsis-associated mortality and improving patient prognosis because severe sepsis cases can lead to organ failure and other life-threatening complications. Diagnosis of Sepsis is challenging in terms of diagnostic accuracy and timeliness due to ambiguous symptoms and individual differences which are captured in heterogeneous varied data sources with irregular time sequences. In recent years, numerous efforts have been made using machine learning methods for sepsis prediction. However, there are still very limited successful industry scale implementations due to limitations of consistency of input data, which often does not fit the characteristics of uncertain time intervals and large number of missing values in real-world critical care settings. In this study, we demonstrate an innovative approach to predict sepsis occurrence in real-time, on heterogeneous sets of variables with multiple time-granularity, using a flexible graph structure to model patient health records. Our modeling task is to predict future sepsis risk at any time after the first 48 hours of admission using observations data from any time window within the past 12 hours. To the best of our knowledge, our proposed approach is the first ever implementation that is dynamic, uses a graph representation to overcome the problem of irregular input features, and offers continuous risk prediction, thereby improving the compatibility of the model in clinical settings. Unlike prior efforts that report results on de-identified curated public datasets with synthetic data filters, our experiments and results are validated by clinical experts, and are based on a large level-1 trauma center’s multi-year real-world longitudinal data.