Multi-classifier disease model for differential diagnosis
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Solution Overview
Problem
Current clinical pathways are isolated and do not effectively account for the complex interconnections of diseases and co-morbidities, leading to inaccurate diagnoses and overlooking rare conditions, and are inflexible and time-consuming to develop and adapt to diverse patient populations and local settings.
Innovation Solution
A multi-classifier disease model using machine learning to construct a comprehensive network of diseases and health variables, allowing for dynamic learning and adaptation to provide differential diagnoses based on patient-specific data, incorporating co-morbidity and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If isolated clinical pathways are used for single diagnosis or disease group, then the protocol is simple and focused, but it fails to cover the entirety of diagnostic space and overlooks rare diseases and co-morbidities
Solution Approach 1:
The patent combines multiple isolated clinical pathways into a single integrated system that can handle multiple diseases and co-morbidities simultaneously. The system merges diagnostic protocols for different disease groups while maintaining their individual diagnostic criteria, enabling comprehensive diagnostic coverage without sacrificing diagnostic accuracy for specific conditions.
Solution Approach 2:
The system creates a universal clinical pathway framework that can adapt to multiple disease types and diagnostic scenarios. Rather than requiring separate specialized pathways for each disease, the system provides a multi-functional pathway that can be configured for various diagnostic needs, covering rare diseases and co-morbidities while maintaining focused diagnostic protocols.
2Reliability
If extensive research is conducted to develop accurate clinical pathways, then the pathway content is accurate and comprehensive, but the development time and resources increase considerably
Solution Approach 1:
The system performs preliminary actions by pre-configuring and storing multiple clinical pathways in an integrated framework before they are needed for actual diagnosis. The pathways are prepared in advance with all diagnostic criteria, tests, and protocols already established, allowing rapid deployment without requiring extensive on-demand research and development.
Solution Approach 2:
The system creates reusable templates and copies of proven clinical pathways that can be replicated and adapted for different diagnostic scenarios. Once a pathway is developed and validated for a specific disease, it can be copied and modified for related conditions, reducing the need to conduct extensive research for each new pathway from scratch.
3Stability of the object's composition
If clinical pathways are carefully developed and established, then the pathway content is accurate and standardized, but modification to reflect latest clinical results or adapt to particular communities is challenging and slow
Solution Approach 1:
The system implements dynamic clinical pathways that can be easily modified and updated while maintaining their core standardized structure. The integrated framework allows administrators to update pathway content, add new diagnostic criteria, and adapt to latest clinical results without disrupting the overall system stability. Changes can be made to individual pathways while other pathways remain stable and operational.
Solution Approach 2:
The system segments the clinical pathway system into independent, modular components that can be updated individually. Each disease pathway, diagnostic protocol, and clinical guideline is separated into discrete units that can be modified, added, or removed without affecting the entire system, enabling rapid adaptation to new clinical evidence or community-specific requirements while maintaining overall system stability.
4Ease of manufacture
If evidence-based pathways consider only average patient parameters, then the pathway development is simplified, but the pathways do not appropriately represent diverse population and cannot be localized to specific areas or hospitals
Solution Approach 1:
The system implements local quality by allowing clinical pathways to be customized for specific patient populations, geographic areas, and hospital settings while maintaining the core evidence-based framework. Each pathway can be configured with local parameters, demographic characteristics, and community-specific clinical practices, enabling pathways to appropriately represent diverse populations without requiring complete redevelopment for each location.
Data Source
AI summary
A system and method for identifying differential diagnoses using a multi-classifier disease model. The method includes constructing the multi-classifier disease model based on at least one disease profile, wherein the multi-classifier disease model is a machine learning network of at least one disease and a plurality of health variables; extracting at least one patient health variable from input patient data, wherein the input patient data indicates a patient condition; applying the multi-classifier disease model to the at least one patient health variable to determine probabilities for the at least one disease; identifying, based on the probabilities of the at least one disease, the differential diagnoses for the input patient data; and providing the differential diagnoses.