TLDR: HypKG is a novel framework that enhances precision healthcare by integrating patient-specific information from Electronic Health Records (EHRs) with general knowledge from Knowledge Graphs (KGs). It uses advanced entity-linking to connect data, models them jointly in a hypergraph structure where patient visits are hyperedges and medical attributes are nodes, and employs hypergraph transformers to learn contextualized representations. Experiments on real-world datasets demonstrate that HypKG significantly improves healthcare prediction tasks by effectively leveraging both KG and patient context.
Knowledge graphs (KGs) are powerful tools in the semantic web, widely used across many fields, especially in healthcare. They organize and link entities like drugs, diseases, and symptoms, providing a structured way to represent factual medical knowledge. However, a significant challenge with traditional KGs in healthcare is their inability to account for crucial patient-specific contexts, such as individual health statuses or medication histories. This lack of context can lead to inaccuracies, as a general recommendation from a KG might not be suitable for a patient with specific conditions or existing prescriptions.
Electronic Health Records (EHRs) offer a rich source of patient-specific data, including diagnoses, medications, and demographic information. This data provides a natural context for general KGs, which is vital for advancing precision healthcare – tailoring medical decisions to individual patients.
To address this, researchers have proposed HypKG, a novel framework designed to integrate patient information from EHRs directly into KGs. The goal is to generate contextualized knowledge representations that lead to more accurate healthcare predictions. HypKG tackles three main challenges in combining KG knowledge with external contexts:
Connecting Knowledge and External Contexts
The first hurdle is linking medical entities from EHRs with those in KGs, as naming conventions can differ. HypKG uses advanced entity-linking techniques, specifically an LLM-based method called PromptLink. This process involves preprocessing EHR attributes, generating embeddings using a pre-trained language model like SAPBERT, and then using a large language model (GPT-4) to semantically link EHR attributes to the most relevant KG entities. This robust linking ensures that patient-specific data is accurately integrated.
Jointly Modeling Knowledge and Contexts
Once linked, HypKG represents both KG knowledge and EHR contextual information within a unified hypergraph structure. Unlike standard graphs where edges connect only two nodes, hypergraphs allow ‘hyperedges’ to connect multiple nodes simultaneously. In HypKG, medical attributes (like diagnoses and medications) are treated as nodes, while each patient’s individual visit or encounter is represented as a hyperedge. This hyperedge connects all the medical attributes relevant to that visit, effectively modeling complex relationships within the patient’s context.
Also Read:
- A New Approach to Updating Knowledge Graphs: GraphDPO for Smarter Unlearning
- Enhancing Knowledge Graph Completion with Complementary Multimodal Data
Learning to Integrate Knowledge and Contexts
The final step involves learning proper contextualized representations. HypKG initializes node embeddings in the hypergraph using existing KG representations. It then employs hypergraph transformers, a type of neural network, to model explicit entity-context relationships and learn intricate entity-entity and context-context relationships from the EHR data. This learning process is guided by downstream prediction tasks, such as predicting health conditions or cognitive impairment. The outcome is patient-specific representations and contextualized KG representations that combine the strengths of both data sources.
HypKG’s core innovation lies in its unified, hypergraph-based framework. It directly integrates patient-specific contexts from EHRs into a general biomedical KG, rather than treating them separately or creating isolated personalized KGs. This approach allows for global modeling of interactions between knowledge and contexts, enabling learning across different users and improving the overall quality and utility of the KG.
In experiments, HypKG was tested using a large biomedical KG called iBKH, contextualized with patient data from two real-world EHR datasets: MIMIC-III and PROMOTE. The results showed significant improvements in healthcare prediction tasks, with an average relative performance gain of 12.15% on MIMIC-III and 9.66% on PROMOTE across various evaluation metrics. HypKG also outperformed traditional methods that model high-dimensional EHR attributes without using KGs. Ablation studies further confirmed the effectiveness of HypKG’s design choices, including its entity linking, KG embedding generation, joint modeling, and hypergraph structure.
Case studies demonstrated that HypKG effectively contextualizes KG knowledge. For instance, it increased the similarity between entity pairs that frequently co-occur in patient contexts, even if they had no direct link in the original KG. An example is the co-occurrence of “Insulin” and “Acetaminophen” in patient records; while not directly interacting pharmacologically, diabetic patients often require both, a contextual relationship HypKG successfully captures. This ability to adjust entity and relation representations based on external contexts enhances the real-world utility of the knowledge graph.
In conclusion, HypKG offers a robust framework for integrating patient-specific information from EHRs with general knowledge from KGs, leading to more accurate and tailored predictions in healthcare. Its hypergraph-based approach effectively captures complex relationships, advancing the field of precision healthcare. For more details, you can refer to the full research paper here.
