Prescriptive Element Selection via Machine Learning and User Feedback
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Solution Overview
Problem
Accurate and informed selection of prescriptive elements in healthcare is challenging due to the need to analyze multiple variables, leading to potential fruitless spending on unnecessary treatments that may worsen health conditions.
Innovation Solution
A system and method that utilize a computing device to receive diagnosis and disease stage descriptors, generate a prescriptive model using machine-learning algorithms, and select a prescriptive element based on user implementation inputs, including a loss function minimization process to optimize treatment selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple variables are analyzed to select prescriptive elements, then selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex selection process into distinct functional modules: a prescriptive model generation module that creates treatment recommendations, a user implementation response module that collects user feedback on feasibility and preferences, and a machine-learning algorithm module that processes this feedback. This segmentation allows each module to handle specific aspects of the complex analysis independently, improving selection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system implements a feedback mechanism where user implementation responses (including prescriptive element indicators and allocation standard responses) are fed back into the machine-learning algorithm. This feedback loop allows the system to iteratively refine prescriptive element selections based on actual user experiences and outcomes, continuously improving selection accuracy while the learned patterns reduce the complexity of future selection decisions.
2Device complexity
If prescriptive elements are selected without adequate analysis, then system simplicity is maintained, but treatment effectiveness deteriorates
Solution Approach 1:
The system performs preliminary action by generating a prescriptive model before final element selection. This model is created based on initial diagnosis descriptors and disease stage information, providing a preliminary treatment framework. This preliminary model serves as a foundation that guides subsequent more detailed analysis, ensuring that treatment effectiveness is considered early in the process while maintaining system simplicity through structured preliminary planning.
Solution Approach 2:
The system implements self-service through automated machine-learning algorithms that independently process user implementation responses and automatically refine prescriptive element selections. This self-service capability ensures thorough analysis and high treatment effectiveness without requiring proportional increases in system complexity, as the automated learning process handles the analytical burden.
3Adaptability or versatility
If user implementation responses are collected and processed, then treatment personalization is improved, but information processing requirements increase
Solution Approach 1:
The system applies parameter changes by transforming raw user implementation responses into structured parameters that the machine-learning algorithm can efficiently process. User feedback on prescriptive element indicators and allocation standards is converted into quantifiable parameters that capture treatment personalization needs. This parameter transformation allows the system to achieve high treatment personalization while managing information processing requirements through standardized data representation.
Data Source
AI summary
A system and method for selecting a prescriptive element based on user implementation inputs is illustrated. The system comprises a computing device configured to receive at least a diagnosis descriptor containing a disease classifier and a disease stage descriptor from a user client device, generate a prescriptive model that receives at least a diagnostic descriptor and outputs a plurality of prescriptive elements, wherein each prescriptive element contain a prescriptive allocation resource calculation, transmit the plurality of prescriptive elements to the user client device, receive a user implementation response containing at least a prescriptive element indicator and a prescriptive allocation standard response from the user client device as a function of the transmission, and select a prescriptive element from the plurality of prescriptive elements as a function of the user implementation response and by performing a machine-learning algorithm using a loss function.