Patient Directed Graphs for Personalized Treatment Suggestions
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Solution Overview
Problem
Current medical treatment systems lack efficient methods for generating personalized treatment suggestions that consider multiple diseases and their interrelated health factors, often relying on outdated or non-specific recommendations.
Innovation Solution
A system comprising a processor and memory that generates patient-directed graphs and treatment-directed graphs, applying confidence-level penalties to suggest targeted interventions based on predictive relationships between health factors and diseases, using machine learning and multi-task learning to identify optimal treatment regimens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional medical treatment systems are used, then treatment recommendations can be provided, but the recommendations are non-specific and do not consider multiple diseases and their interrelated health factors
Solution Approach 1:
The system segments the complex task of multi-disease treatment recommendation into distinct components: a patient assessment component that generates a patient directed graph representing multiple diseases and health factors, and a treatment component that generates treatment directed graphs. This segmentation allows each component to specialize in specific aspects of the problem, improving accuracy while managing complexity.
Solution Approach 2:
The patent introduces directed graphs as an intermediary data structure that mediates between patient health data and treatment recommendations. The patient directed graph serves as an intermediary representation of the patient's multi-disease condition, enabling systematic analysis of interrelated health factors before generating treatment suggestions.
2Measurement precision
If personalized treatment suggestions considering multiple diseases are generated, then treatment accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-generating treatment directed graphs and storing them in a database before they are needed for patient recommendations. These pre-computed treatment graphs represent various treatment regimens and their relationships, allowing the system to quickly match them against patient-specific graphs without performing full computational analysis at recommendation time.
Solution Approach 2:
The patent uses copying by creating patient directed graphs that replicate the structure and relationships of treatment directed graphs. This copying approach allows the system to leverage pre-computed treatment knowledge structures and apply them to specific patient cases through graph matching, reducing computational time while maintaining personalization accuracy.
3Reliability
If treatment recommendations are generated without confidence level assessment, then processing is simpler, but reliability of recommendations decreases
Solution Approach 1:
The system changes parameters by introducing confidence levels as quantitative parameters for assessing treatment recommendation reliability. Penalties are applied as adjustable parameters during the weighting of treatment regimens based on their confidence levels, allowing the system to systematically evaluate and rank treatment options while maintaining manageable complexity through parameterized assessment.
Data Source
AI summary
Systems, computer-implemented methods and/or computer program products that facilitate providing treatment suggestions are described herein. In one example, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, treatment directed graphs associated with a plurality of treatment regimens, wherein, in connection with generating respective treatment directed graphs, penalties are applied during weighting of respective regimens as a function of an associated confidence level; identifying a treatment directed graph that corresponds to the patient directed graph; and outputting a set of treatment suggestions associated with the treatment directed graph that corresponds to the patient directed graph.