Hypotension Prediction System Using 3D Biomarker Temporal Representation
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Solution Overview
Problem
Acute hypotensive episodes in intensive care units are difficult to predict in time, leading to insufficient treatment and potential irreversible organ damage or death due to the lack of timely and appropriate intervention.
Innovation Solution
A computer program and system that determines time-varying hypotensive biomarkers and generates an acute hypotension prediction classifier based on a 3D temporal representation of biomarkers, using mathematical indices like RMS, cross-correlation, and Euclidean distances to identify changes before a hypotensive episode, triggering an alarm signal when critical thresholds are crossed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional monitoring methods are used to detect hypotensive episodes, then the monitoring process is simple, but the prediction time is insufficient and treatment cannot be administered in time
Solution Approach 1:
The system performs preliminary analysis of biomarker dynamics before hypotensive episodes occur by establishing reference 3D representations during stable periods and comparing real-time data against these references. This advance preparation enables early detection and prediction, providing sufficient time for treatment intervention before the actual hypotensive event occurs.
Solution Approach 2:
The patent transforms traditional 2D biomarker monitoring into a 3D temporal representation that incorporates time as an additional dimension. This dimensional expansion allows the system to capture dynamic patterns and evolutionary trends of multiple biomarkers simultaneously, significantly improving prediction capability while maintaining manageable system complexity through automated processing.
2Reliability
If multiple biomarkers are monitored to improve prediction accuracy, then the prediction reliability increases, but the system complexity and data processing requirements increase
Solution Approach 1:
The system merges multiple biomarker data streams into a unified 3D temporal representation that integrates information from various physiological parameters. By combining these diverse data sources into a single coherent framework and comparing against a reference model, the system achieves high prediction reliability while avoiding the complexity of analyzing each biomarker separately.
Solution Approach 2:
The patent creates a reference copy of the normal 3D biomarker representation during stable periods. This reference model serves as a template for comparison with real-time data, enabling the system to detect deviations and predict hypotensive episodes reliably. The copying approach simplifies processing by providing a predetermined baseline for automated comparison.
3Speed
If real-time analysis of biomarker dynamics is performed to enable early prediction, then the prediction timeliness improves, but the computational load and processing time increase
Solution Approach 1:
The system performs preliminary computation by establishing the reference 3D representation during stable periods when computational resources can be allocated without time pressure. This advance preparation stores the reference model for rapid comparison with incoming real-time data, enabling fast prediction decisions while minimizing real-time computational energy consumption.
Solution Approach 2:
The patent transforms complex biomarker dynamics into a standardized 3D temporal representation with defined parameters and dimensions. This parameterization allows for efficient computational comparison against the reference model using standardized mathematical operations, reducing the computational energy required for real-time analysis while maintaining prediction speed.
Data Source
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AI summary
A method and system of predicting a hypotensive episode in a patient using one or more time varying hypotension specific biomarkers corresponding to physiological processes in the patient. Data derived from sensors or other measurement devices such as ECG sensors can be used to generate biomarkers. The biomarkers can then be used to generate an acute hypotension prediction classifier, or monitored factor, derived from a three dimensional temporal representation of two or more biomarkers. When the monitored factor exceeds a predetermined threshold the method and system trigger an alarm before an appearance of a hypotensive episode in the patient.