Adaptive Machine Learning Module for Personalized Treatment Schema Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current treatment plans often disregard individual lifestyle and preferences, and lack effective implementation of machine learning in selecting treatment schemas, resulting in limited user choice and personalized approaches.
Innovation Solution
A system and method utilizing a computing device to receive user constitutional and ailment data, employing an adaptive machine learning module to determine remedial processes, derive remedial attribute lists, and generate treatment schemas based on user willingness, selecting the best schema by minimizing a loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning is implemented to select treatment schemas, then treatment personalization and user choice are improved, but system complexity increases
Solution Approach 1:
The system segments the treatment selection process into distinct modules: data collection (constitutional data, ailment state), machine learning processing (adaptive module), attribute derivation (remedial attributes), and schema generation (treatment schemas with loss function optimization). This segmentation allows the complex personalization task to be broken down into manageable, independent components that can be developed and optimized separately.
Solution Approach 2:
The patent introduces a loss function as an intermediary mechanism that bridges the treatment schemas and remedial attributes. The loss function processes and compares the generated schemas against the derived attributes, enabling the system to automatically select the most appropriate treatment schema without requiring complex manual intervention or interpretation of multiple data types.
2Reliability
If multiple treatment schemas are generated and evaluated, then treatment effectiveness is improved, but computational time and processing resources increase
Solution Approach 1:
The system performs preliminary processing by first deriving remedial attributes from user willingness data before generating treatment schemas. This preliminary action prepares the evaluation criteria in advance, allowing the subsequent schema generation and selection processes to operate more efficiently with pre-computed reference standards, thereby reducing overall processing time while maintaining comprehensive evaluation.
Solution Approach 2:
The patent implements a feedback mechanism through the loss function that continuously evaluates generated treatment schemas against the remedial attributes and user willingness data. This feedback loop allows the system to iteratively refine and select the most effective schemas, ensuring treatment effectiveness while the automated feedback process optimizes processing efficiency by eliminating unnecessary iterations.
Data Source
AI summary
A system for selecting a treatment schema based on user willingness includes at least a first computing device configured to receive at least a user constitutional datum and at least a user ailment state from at least a second computing device. At least a first computing device is configured to determine, with an adaptive machine learning module, at least a remedial process label. At least a first computing device is configured to derive a remedial attribute list, wherein the remedial attribute list further comprises a plurality of remedial attribute list entries. At least a first computing device is configured to generate a plurality of treatment schemas. At least a first computing device is configured to select a treatment schema from the plurality of treatment schemas. At least a first computing device is configured to transmit the selected treatment schema to at least a second computing device.