AI Prescriptive Therapy Selection System
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Solution Overview
Problem
Current methods for selecting prescriptive therapies are unreliable and fail to accurately predict individual responses due to the complexity of factors involved, leading to potential adverse reactions and increased healthcare costs.
Innovation Solution
A system and method utilizing a computing device that receives a prescriptive therapy label and generates a prescriptive therapy instruction set through a prescriptive machine-learning process, trained with data entries containing correlated therapy labels, to identify and recommend compatible therapies based on a user's biological profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to select prescriptive therapies, then the selection process is simple, but the accuracy of predicting individual response is low
Solution Approach 1:
The patent replaces traditional mechanical/manual therapy selection methods with an artificial intelligence system that uses machine learning algorithms to analyze biological profiles and predict individual responses to prescriptive therapies. This substitution enables accurate prediction of therapy effectiveness by processing complex biological data patterns that cannot be analyzed through conventional methods.
2Reliability
If traditional therapy selection methods are used, then the system is simple to operate, but adverse reactions cannot be reliably predicted
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously learns from outcomes of prescribed therapies. By analyzing actual patient responses and outcomes, the system refines its predictions and improves the reliability of adverse reaction forecasting. This feedback loop enables the system to adapt and enhance its predictive accuracy over time.
Solution Approach 2:
The system performs preliminary analysis of biological profiles and potential therapy interactions before prescribing treatment. By conducting comprehensive simulations and predictions in advance, the system identifies potential adverse reactions before they occur, allowing clinicians to select safer therapy options proactively.
3Measurement precision
If comprehensive factors are considered in therapy selection, then prediction accuracy improves, but the complexity of the selection process increases
Solution Approach 1:
The patent enables the system to automatically process and analyze multiple biological factors without requiring manual intervention. The AI system independently evaluates comprehensive patient data, identifies relevant factors, and generates therapy recommendations, eliminating the need for clinicians to manually assess each factor while maintaining high prediction accuracy.
Solution Approach 2:
The patent combines multiple analysis functions including biological profile evaluation, therapy interaction analysis, and outcome prediction into a single integrated AI system. This merging of functions allows comprehensive consideration of all relevant factors while presenting a unified, easy-to-use interface for clinicians.
Data Source
AI summary
A system for informed selection of prescriptive therapies. The system includes a computing device configured to receive compositional training data containing a plurality of unclassified data entries. The system is configured to retrieve a user biological profile and generate an unsupervised machine-learning model that utilizes a biological profile as an input and outputs a therapy response label. The system selects a therapy response model and receives from a remote device a proposed prescriptive therapy. The system creates a therapy response model and identifies a prescriptive therapy label for a proposed prescriptive therapy.