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：确定缺失值多元时间序列数据的临床相关生理状态对于为急性疾病如创伤性脑损伤、呼吸衰竭和心力衰竭提供适当的治疗至关重要。利用非时间聚类或数据插值和聚合技术可能会导致有价值信息的丢失和偏见分析。在我们的研究中，我们应用了SLAC-Time算法，一种基于自监督的创新方法，通过避免插值或聚合来保持数据完整性，为急性患者状态提供更有用的表示。通过使用SLAC-Time对大型研究数据集中的数据进行聚类，我们确定了三种不同的创伤性脑损伤生理状态及其特定的特征配置文件。我们使用了各种聚类评估指标，并加入了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，我们发现了特定的临床事件和干预措施如何影响患者的状态和状态转换。