CitiusTech Knowledge Graph Solution

"The healthcare industry faces a unique challenge and opportunity in the vast amount of unstructured data used by clinicians to make critical decisions. This multimodal data, which includes various formats such as documents, images, notes, clinical guidelines, and ontologies, further complicates integration due to its diverse nature. Lack of interoperability leads to incomplete medical observations and suboptimal care decisions. Regulatory complexity is rapidly evolving, making compliance challenging across different jurisdictions

Unlike traditional databases CitiusTech's Knowledge Graphs for Codifying Proprietary Knowledge captures relationships between entities, making them particularly valuable for enhancing RAG (retrieval-augmented generation) systems and reducing LLM hallucinations

Solution Features:

Automated Knowledge Graph Generation: Utilizes Generative AI to automate the creation and maintenance of knowledge graphs, reducing manual effort and accelerating time-to-insight.

Semantic Enrichment: Applies meaning and structure to raw data, facilitating better insights and analytics.

Context-Aware Retrieval: Knowledge graphs provide semantic context, improving the relevance and accuracy of RAG-generated responses

Dynamic Query Optimization: Enhances RAG models by incorporating graph-based reasoning to disambiguate terms and prioritize critical information

Multimodal Data Support: Integrates data fromsuch as documents, images, notes, clinical guidelines, and ontologies for comprehensive information retrieval

Real-Time Insights: Provides on-demand access to unified knowledge across clinical, operational, and administrative functions

How does the Solution work?

Data Ingestion and Integration The platform ingests data from structured (databases, EHRs), semi-structured (JSON, XML), and unstructured (clinical notes, images) sources

Semantic Enrichment and Graph Construction LLMs analyze text and structured data to detect entities, relationships, and contextual patterns

Data Unification and Contextualization The knowledge graph unifies data across sources, into a single, coherent knowledge base

Advanced Querying and Analytics Knowledge graphs serve as a context layer for RAG models, enhancing response accuracy. Graph-based retrieval ensures that answers reflect the most relevant and up-to-date information

Continuous Learning and Adaptation The platform continuously refines the knowledge graph as new data is ingested, ensuring models stay accurate and up-to-date

Value Proposition

Improved Decision-Making: Delivers richer, context-aware insights for clinical, operational, and strategic decisions

Enhanced RAG Performance: Boosts the relevance and precision of LLM-generated responses by adding semantic context

Operational Efficiency: Reduces manual efforts in graph creation and data unification with LLM-powered automation

Future-Ready Architecture: Adapts easily to new data sources, use cases, and evolving AI capabilities

Healthcare-Specific Insights: Aligns with healthcare standards to support use cases like population health, value-based care, and clinical research

Q&T solution integration: Real-time metrics evaluation to compare different retrieval methods with baseline RAG. Ability to choose the most useful retrieval method for proprietary content stored as KG

AWS Tech stack used: Amazon Neptune, S3, DynamoDB, Textract, AWS Bedrock, AWS Lambda and Kendra Services "