Minsol Kim, Li-wei Lehman. “Characterizing Dynamics of Vital-Sign Signals Using Switching State Space Modeling to Assess Fluid Responsiveness in ICU Patients,” American Medical Informatics Association (AMIA). December 2023.
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Minsol Kim, Li-wei Lehman. “Characterizing Dynamics of Vital-Sign Signals Using Switching State Space Modeling to Assess Fluid Responsiveness in ICU Patients,” American Medical Informatics Association (AMIA). December 2023.
Fluid responsiveness is crucial when administering fluids to patients, as it must be tailored to the patient's personal needs and medical state. Traditional "one-size-fits-all" approach to patient treatment often falls short of achieving optimal outcomes. To address this, our main goal is to create a robust machine-learning algorithm to offer valuable insights to . Specifically, we aim to explore whether the state representation derived from the can predict fluid responsiveness in various scenarios using minute-by-minute, high-resolution data from PhysioNet MIMIC III. Ultimately, this research aims to uncover dynamic markers of various healthcare professionals making treatment decisions. Switching AutoRegressive (AR) model and states indicative treatment outcomes.