State Transition Graphs for Clinical Pathway Analysis
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Solution Overview
Problem
Current healthcare systems lack data-driven approaches for personalized care pathway management due to insufficient graphical and data structures to store and associate historical pathway data, and inadequate analytical methods to utilize such structures, leading to variations in treatment outcomes and inaccuracies in genome analysis.
Innovation Solution
A computer-implemented method for constructing state transition graphs using treatment history and clinical data to generate individual treatment pathways, aligning and merging them to create a graphical structure that represents treatment workflows, allowing for predictive modeling of clinical phenotypes and outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single linear reference genome is used for alignment, then the reference genome structure is simple, but alignment accuracy decreases due to misalignment and non-alignment in diverse genomic regions
Solution Approach 1:
The patent segments the reference genome into multiple alternative sequences (alternative loci) that represent different haplotypes and genomic variations. This segmentation allows reads to be aligned against multiple possible sequences rather than a single linear reference, improving alignment accuracy in diverse genomic regions while maintaining manageable complexity through structured organization of alternatives.
Solution Approach 2:
The patent transitions from a one-dimensional linear reference genome to a multi-dimensional graph structure where nodes represent genomic sequences and edges represent alternative paths. This dimensional transformation enables representation of complex haplotype structures and genomic variations that cannot be captured in a linear format, significantly improving alignment precision for diverse samples.
2Device complexity
If no graphical structure is used to store pathway data, then data storage is simple, but personalized care pathway management becomes impossible due to inability to associate historical pathway data
Solution Approach 1:
The patent introduces a graph structure as an intermediary data structure that connects treatment events, clinical data, and patient pathways. Nodes in the graph represent treatment events or clinical states, while edges represent transitions and associations. This intermediary structure enables complex queries and analyses of personalized care pathways by providing a visual and computational framework to traverse and analyze historical pathway data.
Solution Approach 2:
The graph data structure serves multiple functions simultaneously: it stores historical pathway data, visualizes treatment sequences, enables predictive modeling, and supports personalized care planning. This multi-functional structure replaces multiple separate data storage and analysis systems, providing adaptability for various clinical applications while maintaining a unified approach to pathway management.
3Ease of manufacture
If the human reference genome is based on consensus of few individuals, then the reference construction is simple, but it fails to capture rich diversity of sequences in the human population
Solution Approach 1:
The patent merges multiple individual genomes and their variations into a unified graph structure that preserves the diversity of all contributing samples. Instead of creating a single consensus sequence that loses individual variations, the graph structure combines multiple haplotypes and alternative sequences, allowing the reference to represent rich genomic diversity while maintaining a manageable unified structure through graph-based organization.
Data Source
AI summary
A computer-implemented method for constructing a state transition graph, wherein the method includes obtaining data that includes treatment history and clinical data of a cohort of patients; and generating, by the one or more computing devices, individual treatment pathways for individual patients of the cohort of patients using the treatment history and clinical data for the individual patients; wherein the individual treatment pathways are generated using user-defined parameters including: one or more qualifying events; one or more response states to the one or more qualifying events; and one or more reversible or collapsible events. The method additionally includes constructing a state transition graph that represents multiple aligned and merged individual treatment pathways including the one or more qualifying events, the one or more response states to the one or more qualifying events and the one or more reversible or collapsible events.