Personalized Disease Progression Model Using Genetic Data
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Solution Overview
Problem
Current disease progression modeling approaches are not personalized, instead using uniform trajectory patterns at a population level, which fails to account for individual genetic and environmental factors leading to heterogeneous disease progression in chronic diseases.
Innovation Solution
A method for personalized disease progression modeling that utilizes patient data including genetic and clinical observations to build a personalized framework for unique patient groups, using both supervised and unsupervised computational approaches to determine disease stages and progression patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a uniform trajectory pattern at population level is used to model disease progression, then the modeling approach is simple and computationally efficient, but it fails to account for individual genetic and environmental factors leading to heterogeneous disease progression
Solution Approach 1:
The patient population is segmented into distinct subgroups based on genetic profiles and disease characteristics. The model divides the heterogeneous population into homogeneous clusters, allowing each subgroup to have its own personalized progression trajectory while maintaining overall population-level insights.
Solution Approach 2:
The modeling approach applies local quality by allowing different progression patterns for different patient subgroups. Each cluster receives customized progression models that reflect their specific genetic and clinical characteristics, rather than applying a single uniform model to all patients.
2Measurement precision
If personalized disease progression modeling is implemented to account for genetic and environmental factors, then the accuracy and clinical relevance of disease trajectories improve, but the computational complexity and data processing requirements increase
Solution Approach 1:
The model incorporates multiple parameters including genetic markers, environmental factors, and clinical observations to dynamically adjust disease progression predictions. By changing and integrating multiple parameters, the model achieves higher precision in predicting individual patient trajectories.
Solution Approach 2:
The personalized model integrates multiple data types (genetic data, environmental exposures, clinical measurements) into a composite framework. This composite approach combines heterogeneous data sources to create a comprehensive view of disease progression for each patient subgroup.
3Loss of information
If multiple data types including static genetic data and continuous clinical observations are integrated, then the comprehensiveness of the disease model improves, but the difficulty of detecting and measuring relationships between data types increases
Solution Approach 1:
The model merges static genetic data with continuous clinical observations into a unified personalized progression framework. By combining these different data types, the model retains comprehensive information about patient characteristics and disease evolution without losing insights from either data source.
Data Source
AI summary
A method, computer system, and a computer program product for personalized disease progression modeling is provided. The present invention may include receiving patient data, wherein the patient data is comprised of at least genetic data and clinical observations. The present invention may include building a personalized disease progression framework using one or more patient groupings and one or more disease progression pathways. The present invention may include determining a disease stage and progression pattern for each of the one or more patient groupings using a personalizes disease progression model.