Modular Cardiac Event Detection for Selective Model Updates
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Solution Overview
Problem
Existing medical systems face challenges in accurately detecting cardiac events with high resource utilization and inefficient update mechanisms, leading to potential false determinations and increased operational costs.
Innovation Solution
Implementing a modular machine learning architecture in medical devices that classifies cardiac events using independent modules, allowing for selective updates and reduced resource consumption, enabling accurate detection with minimal computational and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a comprehensive machine learning model is used to detect multiple cardiac event types, then detection coverage is improved, but device complexity and resource utilization increase
Solution Approach 1:
The patent divides a comprehensive machine learning model into multiple independent specialized models, each dedicated to detecting a specific cardiac event type (e.g., atrial fibrillation, ventricular tachycardia, asystole). This segmentation allows the system to maintain high detection coverage across multiple event types while reducing the complexity and resource requirements of each individual model, as each specialized model can be optimized independently for its specific detection task.
2Measurement precision
If the entire machine learning model is updated to improve detection accuracy for one cardiac event type, then accuracy is improved, but update time and computational resources increase
Solution Approach 1:
The patent structures the machine learning system as independent modular models for different cardiac event types, enabling selective updates. When detection accuracy needs improvement for a specific event type, only the relevant specialized model needs to be retrained and updated, rather than retraining the entire comprehensive model. This significantly reduces update time and computational resource requirements while maintaining or improving detection accuracy for the target event type.
3Use of energy by moving object
If multiple cardiac event types are detected using a single model, then resource utilization is reduced, but detection accuracy decreases
Solution Approach 1:
The patent implements a segmented approach where multiple specialized machine learning models are deployed independently, with each model optimized for detecting a specific cardiac event type. This allows the system to achieve high detection accuracy for each event type by dedicating specialized computational resources to each detection task, while the overall resource utilization is managed efficiently through selective deployment and execution of only the relevant models based on the patient's specific needs.
4Device complexity
If a non-modular machine learning architecture is used, then device complexity is reduced, but adaptability and update efficiency worsen
Solution Approach 1:
The patent employs a modular architecture where the machine learning system is divided into independent, interchangeable components (specialized models for different cardiac event types). This modular structure enhances adaptability and update efficiency, as individual models can be developed, trained, validated, and deployed independently. The modular design allows for flexible customization based on patient needs and enables efficient updates of specific models without affecting the entire system, despite the increased architectural complexity.
Data Source
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AI summary
This disclosure is directed to systems and techniques for detecting change in patient health based on a modular machine learning architecture ensembling different cardiac events. In one example, a medical system is configured to: detect a cardiac event type for the patient based on a classification of the patient physiological data in accordance with a modular machine learning architecture, wherein the modular machine learning architecture comprises, for each of a plurality of cardiac event types, an ensemble that comprises a current component model for classifying the cardiac EGM data as evidence of that respective one of the plurality of cardiac event types; and generate for display output data indicative of a positive detection of the cardiac event type.