Prescriptive Therapy Instruction Sets Using Tolerance Feedback
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Solution Overview
Problem
Current methods for selecting prescriptive therapies are unreliable, leading to potential adverse reactions and increased healthcare costs due to the complexity of factors involved in individual responses to treatments.
Innovation Solution
A system and method utilizing a computing device to receive subject prescription and user feedback data, train a prescriptive therapy instruction set machine learning model, and generate therapy instructions through a large language model, accompanied by natural language explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used for selecting prescriptive therapies, then the process is simple, but the accuracy and reliability of therapy selection deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/manual therapy selection processes with an automated machine learning system. The ML model automatically analyzes patient data, therapy outcomes, and feedback to generate personalized therapy recommendations, substituting human decision-making with an intelligent computational system that improves reliability while managing complexity through automation.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between patient data and therapy selection. This intermediary processes complex multi-factor inputs including genetic data, lifestyle factors, and therapy feedback, transforming raw data into actionable therapy recommendations that improve selection accuracy without requiring direct human analysis of all factors.
2Measurement precision
If multiple factors are considered for therapy selection, then the personalization and accuracy improve, but the complexity of the selection process increases
Solution Approach 1:
The patent creates a universal machine learning model that handles multiple types of input data (genetic information, lifestyle factors, therapy feedback) through a single integrated system. This multi-functional approach allows the system to process diverse data types uniformly, improving prediction accuracy while managing complexity through standardized processing pipelines rather than separate analysis methods for each data type.
Solution Approach 2:
The patent transforms complex multi-factor patient data into standardized numerical parameters that the machine learning model can process. By converting qualitative factors (lifestyle, genetics) and quantitative feedback into uniform parameter formats, the system achieves precise therapy response predictions while simplifying the underlying data processing complexity through parameter standardization.
3Object-affected harmful factors
If unreliable therapy selection methods are used, then the process is quick and simple, but adverse reactions and healthcare costs increase
Solution Approach 1:
The patent applies preliminary anti-action by using the machine learning model to predict and prevent adverse reactions before they occur. The system analyzes patient-specific factors and therapy history to identify potential adverse outcomes in advance, generating therapy recommendations that proactively avoid harmful effects rather than reacting to problems after they manifest.
Solution Approach 2:
The patent implements a feedback mechanism where user responses and therapy outcomes are continuously collected and fed back into the machine learning model. This feedback loop allows the system to learn from actual therapy results, improving its ability to predict and prevent adverse reactions over time while refining personalized therapy recommendations based on real-world performance data.
Data Source
AI summary
A system for determining a prescriptive therapy instruction set may include a computing device configured to receive a first subject prescription datum associated with a first subject; receive a first subject tolerance datum associated with the first subject; determine a prescriptive therapy instruction set by training a prescriptive therapy instruction set machine learning model on a training dataset including a plurality of example subject prescription data and subject tolerance data as inputs correlated to a plurality of example prescriptive therapy instruction sets as outputs; and generating the prescriptive therapy instruction set as a function of the subject prescription datum and the subject tolerance datum using the trained prescriptive therapy instruction set machine learning model; receive a second subject tolerance datum associated with the first subject; and retrain the prescriptive therapy instruction set machine learning model as a function of the second subject tolerance datum.