Personalized Drug Contraindication Detection via Knowledge Graph Traversal
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Solution Overview
Problem
Current methods for determining drug contraindications are inadequate as they do not account for individual patient-specific physiological characteristics, leading to missed potentially harmful drug interactions and reliance on trial-and-error approaches that are time-consuming and resource-intensive.
Innovation Solution
A knowledge graph data structure is used, with nodes representing proteins, biological processes, and drugs, and edges weighted based on medical data associated with a user identifier, allowing for biased random traversals to identify contraindications personalized to the patient's physiological state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generalized contraindication evaluation methods are used, then the evaluation process is simple and quick, but the accuracy of treatment selection is reduced and patient-specific contraindications are missed
Solution Approach 1:
The patent segments the evaluation system into distinct functional modules: a knowledge graph construction module that integrates multiple data sources (drug databases, biomedical literature, clinical trial data), a patient data processing module that extracts and standardizes individual patient characteristics, and a contraindication analysis module that performs personalized risk assessment. This modular segmentation enables high measurement precision through comprehensive data integration while managing system complexity through organized functional decomposition.
Solution Approach 2:
The patent transitions from traditional one-dimensional contraindication checking (simple drug-drug interaction lists) to multi-dimensional personalized assessment by incorporating diverse data dimensions including patient genetics, comorbidities, concurrent medications, and individual physiological parameters. This dimensional expansion enables accurate identification of patient-specific contraindications that generalized methods would miss, resolving the contradiction between precision and complexity.
2Loss of time
If trial-and-error approaches are used to identify contraindications, then the evaluation process is resource-intensive and time-consuming, but comprehensive patient-specific analysis can be achieved
Solution Approach 1:
The patent implements preliminary action by pre-construction of a comprehensive knowledge graph that integrates drug interaction data, biomedical knowledge, and clinical evidence before patient evaluation. The system pre-processes and structures vast amounts of contraindication information into an accessible graph format, enabling rapid querying and analysis during actual patient assessments. This preliminary preparation eliminates the need for time-consuming trial-and-error approaches while maintaining complete and reliable contraindication detection.
Solution Approach 2:
The system incorporates feedback mechanisms where contraindication analysis results are continuously refined based on patient responses and clinical outcomes. The knowledge graph is dynamically updated with new evidence and patient-specific data, improving the reliability of contraindication detection over time while maintaining efficient processing speeds through learned patterns from previous assessments.
3Adaptability or versatility
If individualized knowledge graph traversal is performed, then personalized contraindication identification is achieved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by performing knowledge graph traversal selectively rather than exhaustively. The system identifies and prioritizes relevant patient-specific pathways based on individual characteristics, focusing computational resources on high-probability contraindication routes. This selective traversal approach maintains high personalization and adaptability while significantly improving processing efficiency by avoiding unnecessary exploration of irrelevant graph pathways.
Data Source
AI summary
Various embodiments of the present disclosure disclose generating contraindication alert communications. A knowledge graph data structure, including a graph-based representation associated with a user identifier and having nodes and edges, is accessed. Edge weights are adjusted based on medical data associated with the user identifier. One or more sequential traversals of the knowledge graph data structure are performed until an equilibrium condition is met. Based on determining that a subset of nodes is associated with visit tallies totaling more than a threshold proportion of all node visits associated with the one or more sequential traversals, a contraindication alert communication, which includes representation of a biological effect for the user identifier, can be generated and transmitted.