Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.

翻译：从含缺失值的多元时间序列数据中确定临床相关的生理状态，对于为创伤性脑损伤（TBI）、呼吸衰竭和心力衰竭等急性疾病提供恰当治疗至关重要。采用非时序聚类或数据插补与聚合技术可能导致有价值信息的丢失及分析偏差。本研究应用SLAC-Time算法——一种基于自监督的创新方法，通过避免插补或聚合来保持数据完整性，从而为急性患者状态提供更有价值的表征。通过使用SLAC-Time算法对大型研究数据集中的数据进行聚类，我们识别出三种独特的TBI生理状态及其特定的特征图谱。我们采用多种聚类评估指标，并整合临床领域专家的意见，对所识别的生理状态进行验证与解读。此外，我们还发现了特定临床事件和干预措施如何影响患者状态及状态转换。