A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics
The utilization of large language models (LLMs) in the Healthcare domain has generated
both excitement and concern due to their ability to effectively respond to freetext queries with …
both excitement and concern due to their ability to effectively respond to freetext queries with …
A survey on semantic processing techniques
Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …
era of powerful pre-trained language models and large language models, the advancement …
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 …
PROMISE: A pre-trained knowledge-infused multimodal representation learning framework for medication recommendation
Abstract Electronic Health Records (EHRs) significantly enhance clinical decision-making,
particularly in safe and effective medication recommendation based on complex patient …
particularly in safe and effective medication recommendation based on complex patient …
Evaluation and Analysis of Large Language Models for Clinical Text Augmentation and Generation
A Latif, J Kim - IEEE Access, 2024 - ieeexplore.ieee.org
A major challenge in deep learning (DL) model training is data scarcity. Data scarcity is
commonly found in specific domains, such as clinical or low-resource languages, that are …
commonly found in specific domains, such as clinical or low-resource languages, that are …
Multi-aspect Knowledge-enhanced Hypergraph Attention Network for Conversational Recommendation Systems
X Li, Y Zhang, Y Huang, K Li, Y Zhang… - Knowledge-Based Systems, 2024 - Elsevier
Conversational recommendation systems (CRS) aim to proactively elicit user preferences
through multi-turn conversations for item recommendations. However, most existing works …
through multi-turn conversations for item recommendations. However, most existing works …
From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR
Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical
research and practice. In recent years, deep learning models have been applied to EHRs …
research and practice. In recent years, deep learning models have been applied to EHRs …
Processing of clinical notes for efficient diagnosis with feedback attention–based BiLSTM
Predicting a patient's future health state through the analysis of their clinical records is an
emerging area in the field of intelligent medicine. It has the potential to assist healthcare …
emerging area in the field of intelligent medicine. It has the potential to assist healthcare …