Graph-Based Predictive Inference for Drug Regimen Safety
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Solution Overview
Problem
Current systems lack precision in predicting adverse effects of drug regimens consisting of multiple drugs, as they do not account for individual patient demographics and clinical factors, leading to inaccurate adverse event information.
Innovation Solution
A graph-based medical prediction system that constructs patient cohorts based on demographic and clinical similarities, using a predictive database to determine related drug profiles and adverse event occurrence profiles, improving the accuracy of drug information and patient tolerance assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction systems are used for drug regimen analysis, then the system complexity is low, but the measurement precision of adverse effect predictions is insufficient
Solution Approach 1:
The system segments the medical prediction problem into distinct graph-based modules: patient cohort identification, drug regimen analysis, and adverse effect prediction. Each module processes specific relationships (intake relationships, historical relationships, encoding relationships) independently before integrating results, thereby improving prediction accuracy while managing system complexity through structured decomposition
Solution Approach 2:
The patent transitions from traditional linear prediction models to a multi-dimensional graph-based structure that incorporates patient demographics, clinical factors, drug interactions, and historical data simultaneously. This dimensional expansion enables comprehensive analysis of multiple variables and their interrelationships, significantly enhancing prediction precision beyond conventional single-factor approaches
2Reliability
If graph-based predictive databases with multiple relationship types are implemented, then the reliability of adverse effect predictions improves, but the device complexity increases
Solution Approach 1:
The graph-based predictive database employs universal node and relationship types that serve multiple functions: patient nodes store demographic and clinical information, drug nodes capture medication details, and relationships encode various interactions (intake, historical, encoding). This multi-functional design allows a single unified structure to handle diverse prediction requirements, improving reliability while avoiding the need for separate specialized databases for each function
Solution Approach 2:
The patent introduces intermediate processing layers including patient cohort identification and drug profile determination that mediate between raw database queries and final adverse effect predictions. These intermediary steps organize and filter the complex multi-relationship data before analysis, making the overall system more reliable by ensuring systematic processing while reducing the apparent complexity for end users
Data Source
AI summary
There is a need to perform predictive inference to predict likely adverse events of a drug regimen consisting of multiple drugs. In one example, a method includes determining, based at least in part on a graph-based predictive database, one or more predictive categories for each patient node of a plurality of patient nodes; determining, based at least in part on each one or more predictive categories for a patient node and each of one or more patient attribute nodes for a patient node, a related patient cohort for the primary patient node, wherein the related patient cohort comprises the primary patient node and one or more related patient nodes; determining, based at least in part on one or more intake relationships for each patient node in the related patient cohort, a first related drug profile for the primary patient node; and generating a first prediction interface based at least in part on the first related drug profile.