State Transition Graphs for Clinical Pathway Alignment
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Solution Overview
Problem
Current healthcare systems lack data-driven approaches for personalized care pathway management due to inadequate graphical and data structures for storing and associating historical pathway data, and insufficient 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
1Measurement precision
If a single linear reference genome is used, then the reference genome is simple and easy to manage, but it causes misalignment and non-alignment of reads, resulting in false positives and false negatives in genome analysis
Solution Approach 1:
The patent segments the single linear reference genome into a graph structure with multiple paths, where each path represents a different reference sequence. This segmentation allows reads to be aligned to the most appropriate reference path, improving alignment accuracy and reducing false positives and false negatives in genome analysis.
Solution Approach 2:
The patent creates a composite reference structure that combines multiple reference genomes into a single graph-based reference. This composite reference integrates the diversity of different individuals while maintaining a unified structure for alignment, resolving the contradiction between reference simplicity and alignment accuracy.
2Reliability
If treatment pathways are customized for each patient, then personalized care outcomes improve, but the complexity of managing and analyzing individual pathways increases
Solution Approach 1:
The patent merges multiple individual treatment pathways into a unified state transition graph that captures common patterns and variations across patients. This merging allows the system to maintain personalized treatment pathways while reducing management complexity through shared structural elements and automated analysis.
Solution Approach 2:
The state transition graph serves multiple functions simultaneously: it represents individual patient pathways, enables predictive modeling, identifies treatment patterns, and supports decision-making. This multi-functionality reduces the need for separate systems, thereby managing complexity while improving treatment outcome accuracy.
3Measurement precision
If historical pathway data is stored and analyzed to enable predictive modeling, then treatment effectiveness can be predicted, but the complexity of data structures and analytical methods increases
Solution Approach 1:
The state transition graph acts as an intermediary structure that transforms raw historical pathway data into a standardized graphical representation. This intermediary format simplifies the data structure while enabling complex analytical methods to operate on a unified framework, thereby improving predictive modeling accuracy without proportionally increasing system complexity.
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.