【Introduction】
Within the vast ocean of medical data lie countless hidden connections and patterns. H.I.T. (Health, Innovation, Technology)’s “Knowledge Graph and Early Warning System Group” is dedicated to unearthing this deep medical knowledge, structuring and visualizing it, and integrating it with advanced AI technologies. Our goal is to build intelligent systems capable of understanding complex health trajectories and predicting potential disease risks. We aim not just to provide information, but to empower medical decision-makers with forward-looking insights, thereby enabling earlier interventions, more precise treatments, and more effective health management.
I. Building the Knowledge Cornerstone of Intelligent Healthcare
H.I.T.’s Knowledge Graph and Early Warning System has demonstrated its unique value in multiple aspects:
- Construction and Application of Large-Scale Medical Knowledge Graphs :
- We have successfully integrated millions of nodes of medical data, encompassing diseases, symptoms, drugs, genes, treatment plans, diet, side effects, and other multidimensional information, to construct a vast and continuously updated medical knowledge graph.
- This knowledge graph is not merely a collection of data but a network of knowledge associations, clearly illustrating the complex relationships between different medical concepts, such as the link between drugs and side effects, or the connection between specific genes and disease susceptibility.
- Visual Aid Suggestion: Demonstrates how to use the knowledge graph (combined with the KG-RAG framework) to extract and generate precise answers from user queries (e.g., “genes associated with Acute Monocytic Leukemia”). The 2-million-node knowledge graph visually represents its scale.
- Deep Integration of RAG (Retrieval Augmented Generation) Technology, Enhancing LLM Professionalism and Credibility :
- We combine our knowledge graph with advanced Retrieval Augmented Generation (RAG) technology to provide a solid professional knowledge backbone for Large Language Models (LLMs). When our AI Agent answers medical questions or generates reports, it prioritizes retrieving the most relevant and authoritative information from the knowledge graph, ensuring the accuracy, professionalism, and traceability of the output.
- Visual Aid Suggestion: The KG-RAG framework clearly shows how the knowledge graph provides context within the LLM processing flow. Indicates that using KG significantly improves G-Eval metrics (such as coherence, consistency, relevance) by an average of 21.3%, proving KG’s optimization effect on LLM performance.
- Development of Multi-Factor Risk Early Warning Models for Chronic Diseases :
- Based on the relational data within the knowledge graph and extensive clinical cases (such as the 2,200 samples of diabetes and kidney disease comorbidity data, and 7 billion tokens of chronic disease health education documents mentioned in the presentation), we have developed multi-factor risk early warning models for specific chronic diseases (e.g., diabetic nephropathy).
- These models can comprehensively analyze a patient’s clinical indicators, lifestyle habits, genetic background, and other factors to identify high-risk individuals in advance, providing a scientific basis for early intervention and personalized prevention.
- Visual Aid Suggestion: In the framework diagram, “Semantic Search” and “Chinese Dialog Agent” rely on models trained with the knowledge graph and related data to understand patient conditions and provide personalized feedback.
- Enhancing Clinical Decision Support and Medical Research Efficiency:
- The knowledge graph provides a powerful decision support tool for clinicians, helping them quickly access relevant case information, the latest treatment guidelines, drug interactions, etc., thereby improving the accuracy of diagnosis and treatment.
- Simultaneously, the structured knowledge graph greatly facilitates medical researchers, allowing them to conduct data mining, hypothesis validation, and new knowledge discovery more efficiently.
II. Smarter Predictions, Broader Coverage
H.I.T.’s Knowledge Graph and Early Warning System will continue to evolve, focusing on:
- Strengthening Dynamic Knowledge Graphs and Real-Time Early Warning Capabilities:
- Achieve near real-time updates for the knowledge graph, rapidly incorporating the latest medical research findings, changes in clinical guidelines, new drug information, etc.
- Combine with streaming data processing technologies to develop early warning models that can perform real-time analysis of individual health data (such as continuous glucose monitoring, wearable device data), enabling early warnings for acute health events (e.g., hypoglycemia, arrhythmia).
- (Reference to latest tech trends): Temporal Knowledge Graphs and event-driven architectures are key technologies for achieving dynamic early warnings.
- Expansion to Multimodal Knowledge Graphs:
- Extend the scope of the knowledge graph from structured and textual data to multimodal information such as images and audio. For example, incorporating medical image features, patterns in pathology images, and emotional acoustic features from voice interactions into the knowledge graph will create more comprehensive patient profiles and disease understanding.
- (Reference to latest tech trends): Multimodal Knowledge Graph Embeddings technology is rapidly developing, aiming to learn unified representations of data from different modalities.
- Explainable Early Warnings and Personalized Intervention Pathway Recommendations:
- Not only provide warning signals but also offer clear, explainable reasons for the warnings, allowing healthcare professionals and patients to understand the sources of risk.
- Based on warning results and individual situations, intelligently recommend personalized intervention measures and health management pathways from the knowledge graph, creating a closed loop from “warning” to “action.”
- Cross-Disease Domain Knowledge Transfer and Rare Disease Auxiliary Diagnosis:
- Utilize the associative nature of the knowledge graph to explore knowledge transfer between different disease domains, such as applying knowledge from common diseases to assist in the diagnosis and treatment exploration of related rare diseases.
- For rare diseases, the knowledge graph can integrate scattered case reports and research literature from around the world, providing valuable reference information for doctors.
- Public Health Knowledge Popularization and Empowerment:
- Develop an easy-to-understand public health education platform based on the knowledge graph to help the public better understand their health conditions, identify false medical information, and improve health literacy.
III. The Data Intelligence Engine Empowering Precision Medicine
As the underlying infrastructure for intelligent healthcare, the Knowledge Graph and Early Warning System holds immense commercial value:
- Core Engine for Clinical Decision Support Systems (CDSS):
- Provide hospitals and clinics with CDSS based on knowledge graphs, integrated into electronic health record systems, offering doctors real-time diagnostic suggestions, medication safety alerts, treatment plan recommendations, etc., to improve medical quality and safety.
- Risk Assessment and Management Tools for InsurTech:
- Offer insurance companies more precise individual health risk assessment models for personalized insurance product design, underwriting, claims management, and proactive health intervention services, reducing payout risks.
- Accelerator for Drug Development and Real-World Studies (RWS):
- The knowledge graph can integrate multi-source data such as clinical trial data, electronic health records, and academic literature to accelerate new drug target discovery, drug repositioning, and post-market real-world efficacy and safety monitoring.
- Public Health Surveillance and Infectious Disease Early Warning Platform:
- Build a knowledge graph and early warning system for public health departments, integrating multi-source data (e.g., symptom reports, social media, environmental data) to achieve early monitoring of infectious disease outbreaks, trend prediction, and resource allocation optimization.
- Knowledge Mid-Platform for Personalized Health Management Services:
- As a B2B service, provide various health management app developers, wearable device manufacturers, and physical examination institutions with powerful knowledge graph API interfaces and early warning model services, empowering their products with intelligence and personalization.
【Conclusion】
At H.I.T., we deeply believe in the power of knowledge. Our “Knowledge Graph and Early Warning System” is not just a technological stack but a systematic precipitation of medical wisdom and its innovative application. We are committed to transforming complex medical data into actionable insights, working with partners to build a safer, more efficient, and more forward-looking intelligent healthcare ecosystem, ultimately benefiting the health of every patient.