Abstract:
In the realm of critical care medicine, the continuous generation of vital signs time series data from Intensive Care Units (ICUs) serves as a pivotal element for clinicians in evaluating patient prognostics and accessing their health status proactively. The abundance of this data necessitates advanced analytical approaches, and the application of artificial intelligence stands out as a potent method for deriving meaningful insights and patterns. This project focuses on employing deep learning techniques, specifically utilizing the BERT model, to discern intricate patterns within the time series data. The ultimate goal is to generate embedding vectors that can be seamlessly integrated into various downstream tasks, including prognostication and predictive analytics. This approach holds the promise of enhancing the efficiency and accuracy of clinical decision-making, thereby advancing the landscape of critical care medicine.
