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：从含有缺失值的多元时间序列数据中识别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病症提供适当治疗至关重要。使用非时间聚类或数据插补与聚合技术可能导致有价值信息丢失及分析偏差。在本研究中，我们应用了SLAC-Time算法——一种创新的基于自监督的方法，该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用的表征。通过在大规模研究数据集中使用SLAC-Time进行聚类，我们识别出三种不同的TBI生理状态及其特定特征轮廓。我们采用多种聚类评估指标，并结合临床领域专家的意见来验证和解释所识别的生理状态。此外，我们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。