Time-Varying Predictive Patterns for Personalized Disease Management
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Solution Overview
Problem
Current healthcare methods use generalized risk factors to evaluate disease status, which are not patient-specific and do not account for time-varying physiopathology, leading to ineffective disease management.
Innovation Solution
A computer-implemented method that identifies global risk factors using training patients' data, aligns longitudinal data with defined time stamps to create a disease progression timeline, positions a target patient on this timeline, and calculates time-varying predictive patterns of risk factors for personalized disease management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generalized risk factors are used to evaluate disease status, then the evaluation process is simple and efficient, but the results lack patient-specific accuracy and do not reflect time-varying disease progression
Solution Approach 1:
The patent segments the disease progression into discrete time-varying risk factors, dividing the continuous disease process into measurable stages. This allows personalized evaluation by breaking down complex disease progression into manageable temporal segments that can be analyzed individually for each patient
Solution Approach 2:
The patent adds a temporal dimension to risk factor analysis by introducing time-varying measurements. Instead of static risk factor profiles, the system measures how risk factors change over time, creating a multi-dimensional view of disease progression that enhances patient-specific accuracy
2Measurement precision
If volume-based population approach is used, then the investigation covers broad population characteristics, but it fails to provide individual-specific value insights
Solution Approach 1:
The patent applies local quality by focusing analysis on individual patient characteristics rather than uniform population averages. Each patient's risk factor profile is customized to their specific disease progression pattern, allowing precise individual-specific evaluation while using standardized measurement protocols
3Reliability
If static risk factor determination is used, then the assessment is quick and straightforward, but it does not reflect the dynamic nature of disease progression
Solution Approach 1:
The patent performs preliminary action by pre-defining the temporal framework and measurement protocols for risk factor analysis. The system prepares time-varying measurement schedules and analysis frameworks in advance, allowing rapid calculation of dynamic risk factors when patient data becomes available without requiring complex real-time computation
Data Source
AI summary
Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.