Graph Artificial Intelligence in Medicine
In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …
neural networks and graph transformer architectures, stands out for its capability to capture …
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications, and thus investigation of deep learning for HOIs has become a valuable …
applications, and thus investigation of deep learning for HOIs has become a valuable …
Graph ai in medicine
In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …
neural networks (GNNs), stands out for its capability to capture intricate relationships within …
Ram-ehr: Retrieval augmentation meets clinical predictions on electronic health records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
Multimodal fusion of ehr in structures and semantics: Integrating clinical records and notes with hypergraph and llm
Electronic Health Records (EHRs) have become increasingly popular to support clinical
decision-making and healthcare in recent decades. EHRs usually contain heterogeneous …
decision-making and healthcare in recent decades. EHRs usually contain heterogeneous …
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
The growing availability of well-organized Electronic Health Records (EHR) data has
enabled the development of various machine learning models towards disease risk …
enabled the development of various machine learning models towards disease risk …
Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction
W Shi, M Murakoso, X Guo… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
With the extensive time and financial requirements incumbent on drug discovery,
computational approaches, such as protein-ligand docking predictions, are increasingly …
computational approaches, such as protein-ligand docking predictions, are increasingly …
Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets
W Shi, M Murakoso, X Guo, L Xiong… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
Emerging infectious diseases, such as coronavirus disease 2019 (COVID-19), pose a major
threat to public health and present a critical challenge for drug discovery. Due to the cost …
threat to public health and present a critical challenge for drug discovery. Due to the cost …