Proactive AI System for Real-Time Prediction and Management of Anesthesia-Induced Hypotension
This is an advanced computational system employing a highly-optimized machine-learning model to provide early and precise prediction of mean blood pressure (MBP) and the associated risk of Post-Induction Hypotension (PIH) in surgical patients. By accurately forecasting hemodynamic instability during a critical phase of anesthesia, the technology enables clinicians to proactively intervene with specific, actionable therapeutic recommendations, which significantly improves patient safety and outcomes.
Description
The core technology is a sophisticated, optimized machine-learning model (such as an Extreme Gradient Boosting or Neural Network model) that is trained on a massive, complex dataset of patient-specific inputs. This dataset includes comprehensive demographic data, preoperative clinical information (like comorbidities and lab results), and real-time peri-induction medication details and dosages (e.g., anesthetic agents like propofol or fentanyl). The system is designed for memory- and compute-efficiency through specialized preprocessing techniques like one-hot encoding and imputation of missing values, ensuring rapid execution on clinical computing systems. The predictive model continuously refines its internal parameters for high precision in forecasting the patient’s Mean Blood Pressure over a predetermined post-induction time interval. Critically, a secondary reinforcement-learning component is integrated to analyze the blood pressure prediction and automatically generate an optimal set of candidate therapeutic interventions. These recommendations, which can include adjusting anesthetic agent dosage, adding an agent, or administering a specific fluid, are then presented via a user interface for clinician guidance or, in certain embodiments, can be automatically applied by an associated anesthesia delivery system.Applications
- Anesthesia Information Management Systems (AIMS) and Electronic Health Records (EHRs) integration for automated data capture, real-time prediction, and reporting.- Direct integration into Anesthesia Delivery Systems and surgical monitoring devices for real-time operation and automated therapeutic control in operating rooms (ORs).
- Development as a Clinical Decision Support (CDS) Software Module for use by anesthesiologists and surgical teams in operating and emergency rooms.
- Application in tele-anesthesia or remote patient monitoring platforms where computation-efficient algorithms and low-latency prediction are critical.
- Use in clinical research and drug development programs to assess the hemodynamic stability profile and dosing of new anesthetic agents.
Advantages
- Provides early and precise prediction of Post-Induction Hypotension (PIH) risk over a specified time interval, enabling proactive instead of reactive care.- Generates specific, actionable candidate therapies (e.g., medication type, dosage adjustment) via reinforcement learning for optimal patient management.
- Supports development of a closed-loop system with the potential for automatic application of life-saving therapeutic interventions.
- Optimized for memory- and compute-efficiency, resulting in improved processing device performance, reduced latency, and faster execution time in a clinical setting.
- Enhances clinical decision-making with a clear visual representation of the blood pressure prediction and corresponding therapies via an intuitive user interface.