Causative Chaining for Prognostic Label Classification
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Solution Overview
Problem
The complexity of physiological data analysis, particularly in machine learning, is exacerbated by the multiplicity of data types and sources, making it challenging to accurately classify prognostic labels and identify causal relationships between them.
Innovation Solution
A system and method for causative chaining of prognostic label classifications, which includes a classification device that receives training data to generate prognostic outputs by correlating physiological state data with prognostic labels, using a prognostic label learner and a causal link learner to identify causes of these outputs, employing unsupervised clustering algorithms, expert inputs, and language processing to categorize and associate data elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types and sources of physiological data are analyzed to improve classification accuracy, then prognostic label classification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex data analysis task into distinct modules: a classification device for prognostic label classification, a causal link learner for identifying causal relationships, and a prognostic label learner for generating outputs. This segmentation allows each module to specialize in specific data types and functions, reducing overall system complexity while maintaining high accuracy through comprehensive multi-source data analysis
Solution Approach 2:
The patent introduces intermediary components that bridge different data sources and analysis functions. The classification device acts as an intermediary between raw physiological data and prognostic labels, while the causal link learner serves as an intermediary between prognostic outputs and causal relationships. These intermediaries simplify the data flow and processing logic, making the system more manageable despite the multiplicity of data types
2Measurement precision
If comprehensive training data including multiple prognostic labels and causal relationships is used, then causal relationship identification accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing training data into structured formats before analysis. The classification device receives and organizes training data with prognostic labels and causal relationships in advance, creating ready-to-process datasets that simplify subsequent analysis. This preliminary organization reduces the complexity of real-time processing while maintaining comprehensive causal relationship identification
Solution Approach 2:
The patent extracts and isolates specific causal relationship information from the comprehensive training data. The causal link learner extracts only the relevant causal connections between prognostic labels, separating this information from the broader dataset. This extraction process simplifies data processing by focusing computation on only the essential causal relationships rather than processing all data uniformly
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.