Chronic Disease Knowledge Graph Visualization for Personalized Management
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Solution Overview
Problem
Existing chronic disease data visualization technologies fail to comprehensively integrate multidimensional patient information, lack user-friendly visualization, and do not adequately evaluate health management effects, leading to non-personalized management plans.
Innovation Solution
A patient data visualization method and system that constructs a chronic disease knowledge graph, combines static and dynamic patient data, and projects it onto a two-dimensional plane using semantic technology and dimensionality reduction, allowing for the visualization of risk factor associations and management plan generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If basic graphs such as line charts and histograms are used to display patient data, then the visualization is simple and easy to understand, but the association between chronic disease risk factors cannot be reflected and multidimensional information cannot be systematically integrated
Solution Approach 1:
The patent merges multiple types of patient data (physical signs, lifestyle factors, genetic information, environmental factors) into a single integrated knowledge graph that preserves both the simplicity of visual display and the richness of associative relationships. The knowledge graph combines structured and unstructured data from multiple sources while maintaining semantic relationships between different risk factors and disease outcomes.
Solution Approach 2:
The patent transitions from traditional two-dimensional charts to a multi-dimensional knowledge graph structure that can represent complex relationships between risk factors, diseases, and treatments. The system projects high-dimensional patient data into visual representations that maintain semantic relationships, using techniques like force-directed layout and hierarchical clustering to preserve association information while remaining visually comprehensible.
2Ease of operation
If only daily physical signs data are displayed to show trends, then the visualization is simple, but evaluation systems for daily management effects are lacking and health management effects cannot be clarified
Solution Approach 1:
The patent implements feedback mechanisms by continuously updating the knowledge graph with new patient data and comparing current health status against historical data and management goals. The system provides automated evaluation of health management effectiveness by analyzing changes in risk factor profiles, treatment responses, and health outcomes, then feeds this evaluation back to both patients and healthcare providers for informed decision-making.
Solution Approach 2:
The knowledge graph serves multiple functions simultaneously: it stores patient data, visualizes health trends, evaluates management effectiveness, identifies risk factors, recommends treatments, and supports clinical decision-making. This multi-functional system eliminates the need for separate evaluation systems while maintaining comprehensive oversight of patient health management.
3Device complexity
If various types of data such as personal physical conditions, exercise and diet habits are not comprehensively considered in health management plans, then the planning process is simpler, but the decision making lacks individualization and patient data are not fully used
Solution Approach 1:
The patent performs preliminary analysis of patient data by constructing the knowledge graph and identifying key risk factors, treatment options, and management strategies before developing specific health management plans. The system pre-processes and structures patient data, pre-identifies relevant clinical guidelines and evidence-based recommendations, and prepares personalized treatment templates that can be quickly adapted to individual patient needs, reducing the complexity of the planning process while maintaining high individualization.
Data Source
AI summary
Provided is a patient data visualization method and system for assisting decision making in chronic diseases. According to the present application, a management data model diagram of a patient on a hyperplane is constructed by constructing a chronic disease knowledge graph, and combining static data and dynamic data of the patient, and then the management data model diagram is projected onto a two-dimensional plane. The difference of the Euclidean distance between features of a patient information model on a two-dimensional plane graph from the distance of standard features is compared, and a management plan is generated and recommended in combination with path node concepts and an attribute relationship between the concepts.