Cardiovascular Early Warning Score Using Trained Classifiers
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Solution Overview
Problem
Early detection and diagnosis of cardiac deterioration are often delayed due to the complexity of existing medical tests and the lack of trained cardiologists in initial assessments, leading to potential life-threatening events.
Innovation Solution
A patient monitor system that includes sensors for vital signs, a microprocessor, and a cardiovascular early warning scoring method, which classifies cardiovascular deterioration using trained classifiers for various types of cardiac issues, providing early warning scores on a display component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized medical tests are used to diagnose cardiac deterioration, then diagnostic accuracy is improved, but detection timing is delayed until advanced stages
Solution Approach 1:
The system performs preliminary assessment using multiple classifiers (myocardial ischemia, left ventricular hypertrophy, systolic heart failure, diastolic heart failure) before specialized tests are ordered. This early classification enables timely intervention while maintaining diagnostic accuracy through trained algorithms that analyze vital signs and ECG data.
Solution Approach 2:
The diagnostic process is segmented into multiple specialized classifiers, each trained to detect specific types of cardiac deterioration. This segmentation allows parallel processing of different deterioration mechanisms, enabling comprehensive assessment without delaying detection.
2Measurement precision
If multiple specialized classifiers are used to classify different types of cardiovascular deterioration, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The complex diagnostic task is divided into separate specialized classifiers for different cardiac deterioration mechanisms (myocardial ischemia, left ventricular hypertrophy, systolic heart failure, diastolic heart failure). Each classifier focuses on specific patterns, improving accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patient monitor system performs multiple functions: it collects vital signs, processes ECG data, runs multiple specialized classifiers, and generates comprehensive diagnostic assessments. This multi-functionality consolidates complex operations into a single integrated system rather than requiring separate devices.
3Loss of time
If early diagnostic tools are deployed, then detection timing is improved, but ease of operation decreases due to interpretation difficulty
Solution Approach 1:
The system performs self-interpreitation through trained classifiers that automatically analyze vital signs and ECG data to generate diagnostic classifications. This eliminates the need for operators to manually interpret complex medical data, making early detection accessible to non-cardiologists while maintaining high diagnostic accuracy.
Solution Approach 2:
The trained classifiers act as intermediaries between raw medical data and clinical decision-making. These algorithms translate complex physiological data into interpretable diagnostic classifications, bridging the gap between sophisticated early detection capabilities and ease of use by general practitioners.
Data Source
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AI summary
A patient monitor (12) includes a display (14) and sensors (20, 22, 24) reading vital signs of a human subject. In a cardiovascular early warning scoring (cEWS) method, the human subject is classified using a plurality of cardiovascular deterioration classifiers (52, 152, 54, 56, 58) each trained respective to a different type of cardiovascular deterioration. The cardiovascular deterioration classifiers operate on a set of inputs characterizing the human subject including the at least one cardiovascular parameter (42) and the at least one respiratory parameter (44), such as tidal volume read by an airflow sensor (24). The cardiovascular early warning scores for the different types of cardiovascular deterioration are outputted on the display of the patient monitor. An empirical myocardial ischemia classifier (52) may be combined with at least one additional ischemia score generated by applying a set of rules (160) or a physiological model (162).