Agent ML Model for Clinical Intervention via Action Pruning
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Solution Overview
Problem
Existing predictive data analysis systems face inefficiencies and reliability issues in generating accurate clinical intervention recommendations due to computational resource intensity and variability in human judgment, leading to suboptimal clinical decision-making.
Innovation Solution
The implementation of an agent machine learning model that determines optimal clinical interventions using a familiarity-adjusted reward function generated by an environment machine learning framework, based on pruned action-state combinations selected from a historical clinical outcome database, to improve predictive output accuracy and reduce computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional predictive data analysis systems are used to generate clinical intervention recommendations, then comprehensive analysis can be performed, but computational resource intensity increases and operational reliability decreases
Solution Approach 1:
The patent extracts and removes computationally expensive operations from the predictive analysis system by implementing action pruning that eliminates redundant state-action combinations before processing. This extraction of unnecessary computational steps reduces resource intensity while maintaining the essential predictive functionality.
Solution Approach 2:
The patent segments the computational process into distinct stages: state representation extraction, action pruning, and predictive analysis. By dividing the workflow into manageable segments with specific functions, the system processes only relevant data at each stage, reducing overall computational resource requirements while improving reliability through structured processing.
2Measurement precision
If all candidate clinical actions are analyzed to ensure comprehensive evaluation, then prediction completeness improves, but computational operations increase
Solution Approach 1:
The patent applies partial action by analyzing only the most relevant subset of candidate clinical actions rather than all possible actions. The action pruning mechanism identifies and processes only those actions with significant impact on patient outcomes, achieving sufficient prediction accuracy without the computational burden of exhaustive analysis.
Solution Approach 2:
The patent implements local quality by applying different processing depths to different action-state combinations. High-priority actions undergo detailed analysis while lower-priority actions receive minimal processing or are pruned entirely. This differentiated approach maintains prediction accuracy for critical decisions while improving overall computational efficiency.
3Stability of the object's composition
If human judgment variability is reduced through standardized protocols, then consistency improves, but adaptability to individual patient cases decreases
Solution Approach 1:
The patent implements feedback mechanisms where the predictive model continuously learns from outcomes of clinical interventions. The system adjusts its predictions based on actual patient responses, maintaining consistent decision-making frameworks while adapting to individual patient characteristics and treatment outcomes through iterative learning.
Solution Approach 2:
The patent applies dynamics by making the decision-making system adaptable rather than static. The machine learning models dynamically adjust their predictions based on current patient state, historical data, and treatment responses. This allows standardized algorithms to flexibly accommodate individual patient variations while maintaining overall consistency in the decision-making process.
Data Source
AI summary
Various embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using an agent machine learning model to determine an optimal clinical intervention based at least in part on the current clinical state and an inferred reinforcement learning policy that is determined based at least in part on a familiarity-adjusted reward function, where the familiarity-adjusted reward function is generated by an environment machine learning framework based at least in part on one or more next state predictions for one or more pruned action-state combinations based at least in part on a historical clinical outcome database, and the one or more pruned action-state combinations are determined based at least in part on one or more pruned clinical actions that are selected from a plurality of candidate clinical actions based at least in part on one or more action pruning criteria.