Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies

Predicting Future Diseases: Integration of Temporal Knowledge Graphs and Medical Ontologies Electronic Health Records (EHRs) are indispensable tools in modern medical institutions. They record detailed health histories of patients, including demographics, medications, lab results, and treatment plans. This data not only improves the coordination an...

Dual-Level Interaction Aware Heterogeneous Graph Neural Network for Medicine Package Recommendation

Research on Medical Package Recommendation Systems: Heterogeneous Graph Neural Network Based on Dual-Level Interaction Awareness With the widespread application of electronic health records (EHRs) in the medical field, how to mine potential and valuable medical knowledge to support clinical decision-making has become an important research direction...

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice Background General medicine, as an important part of community and family healthcare, covers different ages, genders, organ systems, and various diseases. Its core concept is human-centered, family-based, emphasizing long-...

Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Using Large Language Models to Identify Social Determinants of Health from Electronic Health Records Background and Research Motivation Social Determinants of Health (SDOH) have a significant impact on patient health outcomes. However, these factors are often incompletely recorded or missing in the structured data of Electronic Health Records (EHR)...