Speaker
Description
Critical illness (CI) represents a highly significant healthcare issue, not only because of the resources required for its acute management in ICUs, but also in terms of the lingering health effects as a chronic disease on survivors. A central hallmark of CI is immune dysfunction, with both early hyperactivation leading to organ dysfunction and subsequent immunocompetency leading to increased susceptibility to infection and shortened life. The effective control of CI requires personalized precision medicine, which requires the capabilities provided by digital twins compliant with industrial standards and consistent with the definition put forth by the National Academies of Science, Engineering and Medicine (NASEM) in 2024. The Critical Illness Digital Twin (CIDT) is a proposed cyberphysical system compliant with the NASEM report that melds mechanistic models with machine learning and artificial intelligence and intrinsically incorporates control via an ongoing bidirectional sense-actuate connection to a real-world individual patient. We propose embedding the CIDT in an agentic-AI system that can evolve patient state space characterization, forecast capability and control policy optimization with the goal of rescuing ICU patients with immune exhaustion. Key points are the necessary integration of the virtual assets with sensor/assay technology and control modalities flexible enough to accomplish the stated goal.
