Modular Cardiac Event Detection for Efficient ML 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 efficient detection and diagnosis of cardiac events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monolithic machine learning models are used for cardiac event detection, then comprehensive classification capability is achieved, but resource utilization increases and update efficiency decreases
Solution Approach 1:
The patent divides the monolithic machine learning model into multiple independent modules, where each module is dedicated to detecting a specific cardiac event type (e.g., atrial fibrillation, ventricular tachycardia). This segmentation reduces the computational burden on each module, lowering resource utilization while maintaining comprehensive detection capability across all cardiac event types.
Solution Approach 2:
The system dynamically configures and updates individual modules based on detected cardiac events. When a specific cardiac event type is detected, the corresponding module is activated or updated, rather than continuously running all modules. This dynamic approach optimizes resource utilization by activating only the necessary detection capabilities at any given time.
2Adaptability or versatility
If comprehensive machine learning models are deployed to detect all cardiac event types, then detection coverage is improved, but update efficiency deteriorates
Solution Approach 1:
By segmenting the detection system into independent modules for different cardiac event types, the patent enables selective updates of individual modules rather than requiring updates to the entire system. This significantly reduces update time while maintaining the ability to detect all cardiac event types through the collective capability of all modules.
Solution Approach 2:
The system dynamically activates and updates only the modules relevant to currently detected or anticipated cardiac events. This dynamic module activation strategy allows the system to maintain comprehensive event type coverage while minimizing update overhead by only updating necessary modules.
3Measurement precision
If multiple machine learning models run simultaneously for different cardiac events, then classification accuracy is improved, but computational resources increase
Solution Approach 1:
The patent segments the computational workload into specialized modules, where each module is optimized for detecting a specific cardiac event type. This segmentation allows each module to use fewer computational resources while maintaining high classification accuracy for its specific domain, compared to a single comprehensive model attempting to detect all event types.
Solution Approach 2:
The system dynamically activates only the modules necessary for current detection needs based on real-time cardiac data analysis. This dynamic activation reduces overall computational resource consumption by avoiding the continuous execution of all modules simultaneously, while still achieving accurate classification when needed.
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.