Biomedical State Inference Using Progress Indicators
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Solution Overview
Problem
Current computerized methods for inferring the state of a system, such as a patient's health, from biomedical data are inefficient and prone to errors due to uncertain measurements and the inability to mimic expert knowledge, making it difficult to accurately classify healthy or unhealthy states over time.
Innovation Solution
A method that uses progress indicators, such as gradient-filled bars or bean structures, to visualize changes in biomedical measurement data over time by comparing indicator values to control and comparison states, allowing for the calculation of a measure of goodness and difference values to infer the system's state more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computerized methods are used to classify system states from biomedical data, then the analysis speed is improved, but the accuracy and reliability deteriorate due to measurement uncertainty and inability to mimic expert knowledge
Solution Approach 1:
The patent introduces an intermediary visualization layer (progress indicators, difference value displays, goodness measure visualizations) between the raw biomedical data and the final state classification. This intermediary representation allows experts to quickly assess data quality and confidence levels, enabling faster decision-making without sacrificing accuracy by preserving the ability to identify uncertain measurements and conflicting data points.
Solution Approach 2:
The patent replaces the traditional mechanical expert review process with an automated computerized system that calculates difference values, goodness measures, and confidence indicators. This substitution maintains high accuracy by systematically analyzing all data points while improving productivity through automated computation and visual presentation of key metrics that guide expert decision-making.
2Reliability
If multiple biomedical measurement data are collected to improve inference accuracy, then the reliability is improved, but the data complexity and difficulty of interpretation increase
Solution Approach 1:
The patent segments the complex multidimensional biomedical data into distinct visual components: progress indicators for each indicator, difference value representations, goodness measure displays, and confidence level visualizations. This segmentation allows experts to systematically evaluate multiple data sources without being overwhelmed by complexity, as each component can be assessed independently and then integrated to form the final state determination.
Solution Approach 2:
The patent transforms complex multidimensional data relationships into visual dimensions through graphical progress indicators and difference value displays. By representing data quality, confidence levels, and state changes as visual properties (such as bar lengths, color intensities, or positional relationships), the system makes complex data relationships perceivable and interpretable without requiring experts to mentally process high-dimensional numerical data.
3Reliability
If comprehensive biomedical data are analyzed to reduce interpretation errors, then the reliability is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary computational actions by automatically calculating difference values, goodness measures, and confidence indicators before the expert begins interpretation. This preliminary processing prepares the data in an optimized visual format that highlights key features and potential issues, allowing experts to focus their attention on critical aspects rather than examining every raw data point, thereby reducing analysis time while maintaining comprehensive evaluation.
Solution Approach 2:
The patent employs color variations in the visual representation to encode different data attributes such as confidence levels, goodness measures, and state progression. By using color as an additional dimension of information, the system conveys multiple aspects of data quality and reliability through visual cues that can be processed rapidly by the human visual system, reducing the time required to interpret comprehensive biomedical data while improving reliability through enhanced information perception.
Data Source
AI summary
The present disclosure relates to a method, apparatus, system and computer program for inferring a system state over time. Biomedical measurement data is obtained, wherein the data relate to at least one indicator of a system of interest and includes at least two indicator values being indicative of the state of the system of interest and the indicator values are measured at different time points. At least one measure of goodness for the indicator is formed by using values of the indicator of at least one control state and at least one comparison state. Difference values are formed for at least two indicator values with reference to the control and comparison states, and using the at least two difference values a change in value of said indicator is displayed with a progress indicator so that the change over time can be used in inferring the system state. The progress indicator has at least one dimension depending on the value of the at least one measure of goodness.