Dynamic Word Entropy Analysis for Medical Condition Diagnostics
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Solution Overview
Problem
Current technologies lack an effective method to analyze and diagnose medical conditions or assess the impact of interventions using dynamic measures of text or speech richness, which is crucial for monitoring changes in human communication patterns.
Innovation Solution
The system employs Dynamic Word Entropy (DWE) analysis by calculating word entropy values for varying text portions, generating a DWE table, and applying transformation functions to determine characteristics such as speech richness and consistency, enabling the assessment of medical treatment efficacy and other interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static analysis methods are used to assess text or speech, then the analysis process is simple, but the ability to capture dynamic changes in communication patterns is lost
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from static analysis to dynamic analysis through the introduction of Dynamic Word Entropy (DWE). The system calculates word entropy values for varying text portions and generates a DWE table that captures changes over time, enabling the detection of dynamic patterns in communication that static methods cannot identify.
Solution Approach 2:
The patent introduces a new dimension to text analysis by adding the temporal dimension through DWE calculation. Instead of analyzing text as a static whole, the system breaks it down into varying text portions and calculates entropy values across different segments, creating a multi-dimensional view that reveals dynamic characteristics of communication patterns.
2Measurement precision
If comprehensive text analysis is performed to accurately diagnose medical conditions, then diagnostic accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies the Segmentation principle by dividing the text into varying text portions and calculating word entropy for each segment independently. This segmentation allows the system to process text in manageable chunks, reducing computational burden while maintaining diagnostic accuracy through the aggregation of segment-level DWE values into a comprehensive diagnostic assessment.
Solution Approach 2:
The system performs partial analysis by calculating DWE for varying text portions rather than requiring complete analysis of the entire text corpus. This partial action approach enables timely diagnostic assessments by processing representative segments that capture the essential dynamic patterns needed for accurate medical condition diagnosis.
3Reliability
If dynamic word entropy analysis is implemented to monitor communication patterns, then the ability to detect medical conditions improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces DWE tables as an intermediary structure between raw text input and diagnostic output. The DWE table serves as a mediator that transforms complex text data into structured entropy values, simplifying the subsequent analysis process and improving detection reliability by providing a standardized format for comparing communication patterns across different time points.
Solution Approach 2:
The patent replaces traditional mechanical text analysis methods with an information-theoretic approach based on entropy calculation. By substituting conventional linguistic analysis with DWE computation, the system achieves more reliable detection of medical conditions through quantitative measures of information content and pattern variability, reducing dependence on subjective interpretation.
Data Source
AI summary
Devices, systems, and methods for data analysis and diagnostics utilizing Dynamic Word Entropy. A method includes: obtaining a text of a user; determining word entropy values which correspond to different lengths of text-portions of the text of the user; generating a Dynamic Word Entropy table which corresponds to the text of the user; analyzing the table, and determining whether or not the user has a particular medical condition, or determining whether or not a particular intervention has positively affected the user or has negatively affected the user or has not affected the user.