Graph-Based Genomic Data Model for Clinical Decision Support
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Solution Overview
Problem
The integration of genomic testing into daily medical practice is hindered by the complexity and volume of data, with conventional approaches failing to provide actionable information for practitioners due to the static nature of knowledge associations and the inability to handle dynamic changes in genomic alteration data.
Innovation Solution
A graph-based model is employed to organize and analyze genomic data, allowing for the abstraction of gene states into alteration groups and using an inference engine to determine actionable items based on patient context, incorporating trust scores to ensure the reliability of information paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If genomic testing is integrated into daily practice, then better understanding of cancers and more effective treatment approaches can be achieved, but the complexity and volume of data make it difficult to access and review the information needed for treatment decisions
Solution Approach 1:
The patent introduces an intermediary system (graph-based data model and inference engine) that mediates between the complex genomic data and the practitioner. This intermediary automatically processes, integrates, and presents synthesized treatment recommendations, reducing the burden on practitioners to manually navigate complex data while maintaining reliable treatment decisions.
Solution Approach 2:
The patent replaces the manual mechanical process of reviewing and synthesizing genomic data with an automated computational system. The inference engine automatically traverses the graph-based data model to generate treatment recommendations, substituting human cognitive effort with algorithmic processing that can handle the complexity and volume of genomic information efficiently.
2Loss of information
If conventional approaches are used to provide genomic alteration information, then data can be presented, but the information is not readily appreciated by practitioners for diagnostic value and cannot be incorporated into actionable steps
Solution Approach 1:
The system performs self-service by automatically synthesizing genomic alteration information into actionable treatment recommendations without requiring practitioner intervention to interpret the data. The inference engine autonomously traverses the graph-based data model, integrates relevant information, and presents ready-to-use clinical recommendations, making the information both useful and easy to operate with.
Solution Approach 2:
The system performs preliminary action by pre-processing and synthesizing genomic data into actionable recommendations before presentation to the practitioner. The inference engine proactively generates treatment options and clinical insights in advance, so that when the practitioner reviews the results, the information is already organized and ready for clinical decision-making.
3Stability of the object's composition
If knowledge associations are made static to maintain system stability, then the system structure remains consistent, but the system cannot accommodate new discoveries and dynamic changes in genomic alteration data
Solution Approach 1:
The patent implements dynamics by making the knowledge associations in the graph-based data model adaptable and updateable. The system allows new genomic discoveries and alterations to be dynamically added to the graph structure, enabling the system to evolve with new scientific knowledge while maintaining the stability of its core architecture through the consistent graph-based framework.
Data Source
AI summary
Various embodiments provide interfaces to access genomic testing information and incorporate it into daily physician practice. According to one aspect, a graph-based data model is used that may be used to organizes and revise precision medicine knowledge. In one example structure, gene states are abstracted into alteration groups, where alteration groups are built using reverse engineering actionable information and storing that information within the graph-based data structure. Volumes of genomic alterations and associated information (e.g., journal articles, clinical trial information, therapies, etc.) are analyzed and synthesized into actionable information items viewable on an alteration system in a graph-based data format. According to one embodiment, the system can be configured to focus practitioners on discrete portions of the alteration information on which they can act. According to other aspects, curated information is provided on the system to enable practitioners to make informed decisions regarding the implications of the presence of specific genomic alterations.