Healthcare Knowledge Graph for Personalized Insights
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Solution Overview
Problem
The exponential growth of digital healthcare data from various sources poses challenges in extracting meaningful insights due to its heterogeneous and unstructured nature, making it difficult to provide personalized healthcare recommendations efficiently.
Innovation Solution
Constructing a knowledge graph from disparate data sources, including disease, patient, and medicine databases, and using machine learning models to generate insights and recommendations, which are then updated iteratively based on user feedback and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in various structured and unstructured formats across different platforms and systems, then the quantity of healthcare information available increases, but the difficulty of extracting meaningful insights increases
Solution Approach 1:
The patent merges data from multiple heterogeneous sources including EHRs, genomic data, wearable devices, and research databases into a unified knowledge graph. This integration combines structured and unstructured data formats into a single coherent structure, enabling comprehensive analysis while maintaining data diversity and provenance.
Solution Approach 2:
The knowledge graph serves as an intermediary layer between raw heterogeneous data and analytical applications. It transforms diverse data formats into standardized entities and relationships, making the data accessible and interpretable for downstream machine learning models and healthcare insights without losing the complexity of the original sources.
2Measurement precision
If sophisticated techniques are used to extract knowledge from vast digital information, then the quality of insights improves, but the complexity of the system increases
Solution Approach 1:
The system segments the knowledge extraction process into distinct modular components: data ingestion from multiple sources, knowledge graph construction with standardized schemas, machine learning model training, and insight generation. Each module operates independently with well-defined interfaces, reducing overall system complexity while maintaining high insight quality.
Solution Approach 2:
The patent implements preliminary action by pre-constructing the knowledge graph with established medical ontologies and relationships before applying machine learning techniques. This pre-processing creates a structured foundation that simplifies subsequent analysis and reduces the complexity of real-time extraction operations.
3Adaptability or versatility
If data is integrated from heterogeneous data sources into a unified structure, then the ability to provide personalized recommendations improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-integrating data from heterogeneous sources into a unified knowledge graph structure before personalized recommendations are needed. This upfront integration creates reusable data structures that can be quickly queried and processed for individual patient recommendations, reducing real-time processing requirements.
Solution Approach 2:
The knowledge graph is designed to be dynamic and adaptable, allowing efficient querying and updating based on specific patient needs. The structure enables flexible traversal and filtering of integrated data without requiring complete reprocessing, balancing personalization capability with processing efficiency.
Data Source
AI summary
Systems and methods for healthcare insights with knowledge graphs are provided. In one example, a method includes constructing, with a processor, a knowledge graph with data from a heterogeneous plurality of data sources, generating, with the processor, healthcare insights from the knowledge graph, and outputting, to a user device for display to a user, a healthcare recommendation based on the healthcare insights. In this way, various types of data may be used to efficiently provide personalized healthcare recommendations for users.