Research Report on Changes in Functional Connectivity in Post-Stroke Epilepsy (PSE) Patients Based on Source-Level EEG and Graph Theory Research Background Epilepsy has various etiologies, including idiopathic, congenital, head trauma, central nervous system infections, brain tumors, neurodegenerative diseases, and cerebrovascular diseases. Among t...
Changes in Functional Brain Networks in Coronary Heart Disease Patients: Independent Component Analysis and Graph Theory Analysis This article, published in the 229th volume of “Brain Structure and Function” in 2024, explores the changes in functional connectivity (FC) and brain network topology in patients with coronary heart disease (CHD). The st...
Research on Drug-Disease Association Prediction Using Graph Contrastive Learning Based on Layered Negative Sampling The prediction of drug-disease associations (RDAs) plays a critical role in unveiling disease treatment strategies and promoting drug repurposing. However, existing methods mainly rely on limited domain-specific knowledge when predict...
Research Report on Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer: A Study of Multi-View Adaptive Weighted Graph Convolutional Networks Background Introduction Lung cancer is a highly prevalent and poorly prognostic malignant tumor with a persistently high mortality rate. Among all lung cancer patients, Non-Small Cell Lung Cancer ...
Background and Research Motivation It is well-known that food nutrition is closely related to human health. Scientific research has shown that improper dietary nutrition is linked to more than 200 diseases. Especially when considering the metabolic processes of gut microbiota, the complex interactions between food nutrients and diseases become diff...
CIGNN: A Framework for Cuffless Continuous Blood Pressure Estimation Based on Causality and Graph Neural Networks Background Introduction According to data from the World Health Organization (WHO), approximately 1.13 billion people globally are affected by hypertension, and this number is expected to increase to 1.5 billion by 2025. Hypertension is...
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...
EHR-HGCN: A Novel Hybrid Heterogeneous Graph Convolutional Network Method for Electronic Health Record Text Classification Academic Background With the rapid development of Natural Language Processing (NLP), text classification has become an important research direction in this field. Text classification not only helps us understand the knowledge b...
Research Report on the Integration of Medical Relation Extraction and Knowledge Graph-Based Recommendations Background Introduction In the medical field, the explosive growth of literature makes it challenging for researchers to keep up with the latest advancements in their specific areas. From the perspective of natural language processing (NLP), ...
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...