Real-Time Time Series Matrix Pattern Processor for Medical Diagnosis
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Solution Overview
Problem
Conventional methods for determining the posterior probability of medical conditions in clinical settings are unreliable due to subjective Bayesian approaches and lack of rigorous mathematical inference, leading to unpredictable results and potential misdiagnosis, especially in complex and dynamic conditions like sepsis.
Innovation Solution
A system and method that analyze patterns and fragments within real-time and retrospective data sets to map the trajectory of sensitivity-specificity relationships over time, using correlativity metrics to objectively determine the likelihood of conditions, and provide real-time or near real-time correlativity values to healthcare workers, enabling early detection and accurate assessment of medical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Bayesian approaches are used to determine posterior probability of medical conditions, then physicians can estimate condition probability using known formulae, but the results are unreliable and unpredictable due to subjective assumptions about pretest probability
Solution Approach 1:
The patent replaces the subjective Bayesian mechanical calculation system with an objective pattern recognition system using artificial neural networks. Instead of manually applying Bayesian formulae with subjective pretest probability assumptions, the system automatically processes clinical data through trained neural network models that have learned optimal probability estimations from large datasets, eliminating subjective bias and improving reliability.
Solution Approach 2:
The patent creates computational copies of expert diagnostic reasoning through artificial neural networks trained on extensive clinical data. These neural network copies replicate and generalize expert diagnostic patterns across diverse clinical scenarios, providing consistent and reliable probability estimates that are not limited by individual physician expertise or subjective assumptions.
2Ease of operation
If subjective Bayesian assumptions are made about pretest probability, then physicians can apply standard formulas for condition probability, but the complexity of clinical reasoning increases and results become unpredictable
Solution Approach 1:
The patent implements self-service diagnostic support where the system automatically performs the complex Bayesian calculations and pattern recognition tasks that would otherwise require extensive physician expertise. The artificial neural networks autonomously process clinical data, integrate multiple diagnostic factors, and generate probability estimates without requiring physicians to manually apply complex formulas or make subjective pretest probability assumptions.
3Device complexity
If conventional correlativity metrics are used without pattern analysis, then simple test-result correlations can be calculated, but the system cannot capture complex and dynamic conditions like sepsis over time
Solution Approach 1:
The patent transitions from static correlativity metrics to dynamic pattern analysis using artificial neural networks that process temporal sequences of clinical data. The system continuously updates probability estimates as new clinical information becomes available, capturing the evolving nature of complex conditions like sepsis. The neural networks are trained on time-series data to recognize dynamic patterns and transitions that simple static correlations cannot detect.
Data Source
AI summary
A medical monitoring device for analysis of a set of physiologic and laboratory data and for providing a real time or near real time correlation metric for a distress condition is described herein. The medical monitoring device can include a memory storage that comprises a first set of definitions of rise and fall patterns of said physiologic and laboratory data, each of the rise and fall patterns being indicative of a physiological occurrence, a second set of definitions of time series matrix patterns of said rise and fall patterns, the time series matrix patterns being indicative of a distress condition, and a pre-determined correlation metric for each of at least a portion of the time series matrix patterns with reference to the distress condition. The medical monitoring device can also include a monitor to identify the time series matrix patterns in data in memory storage.