Causal Prognostic Label Chaining for Personalized Physiological Analysis
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Solution Overview
Problem
The complexity of analyzing diverse physiological data types and sources poses challenges in automated analysis, exacerbated by expanding knowledge in microscopic and macroscopic physiological states and detection modes.
Innovation Solution
A system and method for prognostic label classifications using a computing device that receives biological extractions, determines prognostic labels, classifies them, and generates user-specific recommendations by adjusting parameters based on baseline rules and modification rules through machine learning and recommendation engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diverse physiological data types and sources are analyzed, then measurement precision and prognostic accuracy are improved, but device complexity and analysis difficulty increase
Solution Approach 1:
The system segments the complex analysis process into distinct functional modules: a machine learning module that determines prognostic labels from biological extractions, a classification module that categorizes labels into predefined groups, and a recommendation engine that generates personalized suggestions. This modular segmentation reduces overall system complexity while maintaining comprehensive data analysis capabilities.
Solution Approach 2:
The patent introduces an intermediary classification layer that maps complex prognostic labels to simplified predefined classifications. This intermediary structure acts as a mediator between raw physiological data and final recommendations, reducing the complexity burden on both the analysis and application layers while preserving measurement precision.
2Loss of information
If comprehensive biological extractions are processed, then information completeness is improved, but loss of time and processing duration increase
Solution Approach 1:
The system performs preliminary classification of prognostic labels into predefined categories before generating recommendations. This preliminary action organizes comprehensive biological data into structured groups in advance, enabling faster retrieval and processing during the recommendation phase without losing physiological information completeness.
Solution Approach 2:
The recommendation engine dynamically adjusts recommendation parameters based on the classified prognostic labels. By changing parameters from generic baseline values to personalized adjusted values based on individual physiological profiles, the system efficiently processes comprehensive data without linearly increasing processing time.
3Adaptability or versatility
If personalized recommendations are generated through parameter adjustment, then adaptability is improved, but device complexity and computational requirements increase
Solution Approach 1:
The recommendation engine implements adaptability by dynamically adjusting recommendation parameters based on classified prognostic labels. The system maintains a library of baseline parameters and modifies them according to individual user profiles and physiological states, achieving personalization through parameter transformation rather than complex algorithmic generation.
Solution Approach 2:
The system applies local quality by tailoring recommendation parameters specifically to each user's classified prognostic profile. Instead of applying uniform complexity across all users, the system adjusts the level of personalization and parameter modification based on individual needs, reducing overall computational burden while maintaining adaptability where required.
Data Source
AI summary
A system for causative chaining of prognostic label classifications includes a classification device configured to receive training data including a plurality of first data entries, each including at least a first element of physiological state data and at least a correlated first prognostic label and a plurality of second data entries, each including at least a second prognostic label and at least a correlated third prognostic label, and to record at least a first biological extraction. The system includes a prognostic label learner configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, and a causal link learner configured to generate at least a second prognostic output causally linked to the first prognostic output as a function of the second training set and the at least a first prognostic output.