Clinical Protocol-Based Federated Learning for False-Alarm Reduction
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Solution Overview
Problem
Traditional federated learning approaches fail to account for varying clinical protocols across different medical facilities, leading to undesirable performance and increased false positives/negatives due to the use of a single central AI model not tailored to individual protocols.
Innovation Solution
A central server generates and deploys multiple AI models specific to different clinical protocols, selecting appropriate models based on properties such as clinical protocols, demographics, and geographical locations, and employs feedback to iteratively refine these models for better alignment with local protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single central AI model is used across all medical facilities, then device complexity is reduced and ease of operation is improved, but measurement precision and reliability deteriorate due to inability to account for varying clinical protocols
Solution Approach 1:
The patent segments the single central AI model into multiple protocol-specific AI models, where each model is trained on data corresponding to a specific clinical protocol. This segmentation allows each model to specialize in its designated protocol, improving measurement precision while the central server manages the segmented models to maintain ease of operation.
Solution Approach 2:
The patent applies local quality by training different AI models with different characteristics tailored to specific clinical protocols. Each model develops local expertise in its designated protocol domain, enabling high measurement precision for protocol-specific predictions while the overall system maintains operational simplicity through centralized management.
2Device complexity
If a single central AI model is used across all medical facilities, then device complexity is reduced, but reliability deteriorates due to increased false positives and false negatives from protocol mismatches
Solution Approach 1:
The patent divides the monolithic AI model into multiple specialized models, each segment handling a specific clinical protocol. This segmentation improves reliability by ensuring each prediction task is handled by a model trained on appropriate protocol data, reducing false positives and negatives, while the central server's segmentation management keeps overall system complexity可控.
Solution Approach 2:
The patent changes the parameter of model specialization by training each AI model on data with specific protocol parameters. This parameter change enables models to adapt to different clinical protocols, improving reliability through protocol-appropriate predictions while maintaining manageable complexity through systematic parameter organization.
3Measurement precision
If protocol-specific AI models are deployed, then measurement precision and reliability improve, but device complexity increases due to multiple models needing management
Solution Approach 1:
The patent introduces a central server as an intermediary that manages the multiple protocol-specific AI models. This intermediary handles model selection, deployment, and coordination, allowing measurement precision to improve through specialized models while the intermediary absorbs the complexity of managing multiple models, keeping the overall system architecture manageable.
Solution Approach 2:
The patent creates a universal central server platform that can manage any number of protocol-specific models through standardized interfaces and processes. This multi-functionality allows the system to handle measurement precision requirements for multiple protocols while maintaining consistent, manageable complexity through the universal management framework.
4Ease of operation
If traditional federated learning is used with a single central model, then ease of operation is maintained, but productivity decreases due to increased human-in-the-loop efforts for verification and correction
Solution Approach 1:
The patent segments the AI modeling into protocol-specific specialized models that automatically adapt to their designated protocols. This segmentation improves productivity by reducing the need for human verification and correction of protocol mismatches, as each model is pre-configured for its specific protocol, while the centralized management maintains ease of operation.
Solution Approach 2:
The patent enables AI models to self-adapt to their specific clinical protocols through automated training on protocol-specific data. This self-service capability improves productivity by eliminating the need for extensive human-in-the-loop verification and correction, as the models autonomously optimize their performance for their designated protocols while maintaining operational simplicity through centralized deployment.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a clinical protocol-based federated learning process. For example, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components that can select, according to a selection criterion applicable to at least one medical facility, a first artificial intelligence (AI) model from a repository comprising a plurality of AI models, deploy the first AI model at the at least one medical facility, access feedback comprising updated parameters of the first AI model generated at the at least one medical facility, and further deploy a second AI model based on the updated parameters and a clinical protocol employed by the at least one medical facility.