Clinical Guidance Reinforcement Learning Framework
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Solution Overview
Problem
Current software systems for clinical guidance face challenges in incorporating prior medical knowledge and accurately measuring the accuracy of decisions, particularly in diagnosing and treating patients, due to opaque reasoning processes and the lack of integration with longitudinal data analysis, leading to inaccuracies in clinical decision support.
Innovation Solution
A reinforcement learning framework that combines experiential case file data sets with an index case file data set to generate a master data set, using a learning identifier calculator to quantify similarity and guide user-defined actions, providing feedback on expected outcomes and proposed interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software systems use machine learning to identify patterns in large medical data sets, then the system can generalize clinical concepts, but the reasoning process becomes opaque and accuracy cannot be quantitatively measured
Solution Approach 1:
The patent introduces a hybrid system that acts as an intermediary between opaque machine learning models and clinical decision-making. The system combines neural network pattern recognition with symbolic AI reasoning engines and knowledge graphs, allowing the transparent reasoning component to mediate and explain the decisions made by the black-box machine learning models, thereby enabling quantitative accuracy measurement while maintaining adaptability.
Solution Approach 2:
The patent creates a composite software architecture that integrates multiple approaches: machine learning models for pattern recognition, symbolic AI for explicit reasoning, knowledge graphs for structured medical knowledge, and reinforcement learning for sequential decision-making. This composite system combines the strengths of each approach while mitigating their individual weaknesses, particularly the opacity of pure machine learning.
2Reliability
If software systems rely on neural networks for decision-making, then pattern matching capability is enhanced, but the system lacks integration with longitudinal data analysis and prior medical knowledge
Solution Approach 1:
The patent merges multiple data sources and processing approaches into a unified system. It combines neural network pattern matching with longitudinal electronic health record analysis, integrates prior medical knowledge from knowledge graphs, and incorporates reinforcement learning policies. This merging ensures that pattern recognition is enhanced while simultaneously maintaining access to and integration of longitudinal data and prior knowledge, preventing information loss.
Solution Approach 2:
The patent creates a universal clinical decision support system that performs multiple functions: pattern recognition in real-time data, longitudinal trend analysis, retrieval of prior medical knowledge, and sequential decision-making. This multi-functional system ensures that no single data type or knowledge source is lost, as each component serves multiple purposes within the integrated architecture.
3Speed
If software systems use traditional machine learning approaches, then computational speed is maintained, but the system cannot provide explainable reasoning for clinical decisions
Solution Approach 1:
The patent segments the decision-making process into distinct components: a fast neural network component for initial pattern recognition and data processing, and a symbolic AI reasoning engine for generate explainable justifications. This segmentation allows the system to maintain computational speed through the efficient neural network while simultaneously providing explainable reasoning through the symbolic component, preventing the loss of interpretability.
Data Source
AI summary
Disclosed are methods and systems to aid medical practitioners with clinical decisions, in which recommendations and other information are derived utilizing a software reinforcement learning framework relating patient information to medical experiential case-files. The decision guidance systems and methods are applicable to medical and other applications.