Classification Device for Prognostic Labels Using Expert Inputs
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Solution Overview
Problem
The complexity of physiological data analysis, particularly in classifying human subjects' conditions, is exacerbated by the multiplicity of data types and sources, making it challenging for automated analysis to accurately predict prognostic labels and ameliorative processes.
Innovation Solution
A system and method utilizing a classification device that records physiological inputs, receives expert submissions, and employs a machine-learning module to generate diagnostic outputs by correlating physiological data with prognostic and ameliorative labels, using training data and expert constraints to refine the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated analysis is used to process physiological data, then productivity is improved, but measurement precision deteriorates due to the complexity of multiple data types and sources
Solution Approach 1:
The patent introduces expert submissions as an intermediary layer between automated machine learning analysis and final diagnostic conclusions. Experts review and constrain machine learning outputs, ensuring accuracy while maintaining automated processing efficiency. This mediator resolves the contradiction by combining automated speed with expert precision.
Solution Approach 2:
The diagnostic process is segmented into distinct phases: automated machine learning analysis generates preliminary prognostic labels, then expert reviewers independently evaluate and constrain these labels. This segmentation allows each component to optimize for its strength - automation for speed, experts for precision - while collectively resolving the contradiction.
2Measurement precision
If multiple data sources and types are integrated to improve diagnostic accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The machine learning module is designed as a universal processor that handles multiple data types (physiological inputs, expert submissions, training data) through a single integrated system. This multi-functionality allows the system to process diverse data sources without proportionally increasing complexity, as the same core algorithms adapt to different input types.
Solution Approach 2:
Expert submissions serve as an intermediary that simplifies complex multi-source data integration. Instead of directly integrating all physiological data sources which would increase complexity, experts translate diverse data into constrained diagnostic labels that the machine learning system can process uniformly, reducing system complexity while maintaining diagnostic accuracy.
3Measurement precision
If expert inputs are integrated with machine learning to improve diagnostic accuracy, then measurement precision is improved, but loss of information increases due to the need to process and reconcile multiple input types
Solution Approach 1:
The system implements feedback loops where expert submissions constrain and refine machine learning outputs. Experts review automated prognostic labels and provide corrective constraints, creating a feedback mechanism that recovers information loss by allowing experts to restore nuanced diagnostic information that may have been simplified or lost during automated processing.
Solution Approach 2:
Expert submissions are incorporated as preliminary constraints before final diagnostic labeling. By having experts establish diagnostic boundaries and constraints in advance, the system preserves critical diagnostic information that would otherwise be lost during automated machine learning processing, as these preliminary expert judgments guide the automated analysis to retain essential clinical nuances.
Data Source
AI summary
A system for classification to prognostic labels using expert inputs includes a classification device. The classification device is designed and configured to record at least a physiological input pertaining to a human subject, receive at least an expert submission pertaining to the human subject, the at least an expert submission including at least a diagnostic constraint, and transmit at least a diagnostic output to a client device. The system includes a machine-learning module operating on the classification device, the machine-learning module designed and configured to receive training data relating physiological input data to diagnostic data and generate at least a diagnostic output using machine learning as a function of the training data, the at least an expert submission and the at least a physiological input.