Graphcare: Enhancing healthcare predictions with personalized knowledge graphs

[PDF] arxiv.org

Ram-ehr: Retrieval augmentation meets clinical predictions on electronic health records

R Xu, W Shi, Y Yu, Y Zhuang, B Jin, MD Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
被引用次数：6 相关文章 所有 3 个版本
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REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

Y Zhu, C Ren, S Xie, S Liu, H Ji, Z Wang, T Sun… - arXiv preprint arXiv …, 2024 - arxiv.org
The integration of multimodal Electronic Health Records (EHR) data has significantly
improved clinical predictive capabilities. Leveraging clinical notes and multivariate time …
被引用次数：4 相关文章 所有 2 个版本
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A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)

L Li, J Zhou, Z Gao, W Hua, L Fan, H Yu… - arXiv preprint arXiv …, 2024 - arxiv.org
Electronic Health Records (EHRs) play an important role in the healthcare system. However,
their complexity and vast volume pose significant challenges to data interpretation and …
相关文章 所有 3 个版本
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Graph learning and its advancements on large language models: A holistic survey

S Wei, Y Zhao, X Chen, Q Li, F Zhuang, J Liu… - arXiv preprint arXiv …, 2022 - arxiv.org
Graph learning is a prevalent domain that endeavors to learn the intricate relationships
among nodes and the topological structure of graphs. Over the years, graph learning has …
被引用次数：6 相关文章 所有 2 个版本
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GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models

P Jiang, J Lin, Z Wang, J Sun, J Han - arXiv preprint arXiv:2402.10744, 2024 - arxiv.org
The field of relation extraction (RE) is experiencing a notable shift towards generative
relation extraction (GRE), leveraging the capabilities of large language models (LLMs) …
被引用次数：2 相关文章 所有 4 个版本
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KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge

P Jiang, L Cao, C Xiao, P Bhatia, J Sun… - arXiv preprint arXiv …, 2024 - arxiv.org
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact
representations of entities and relations within a knowledge graph, facilitating efficient …
相关文章 所有 4 个版本
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From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR

R Xu, Y Lu, C Liu, Y Chen, Y Sun, X Hu, JC Ho… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
相关文章 所有 3 个版本
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EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling

Y Zhu, C Ren, Z Wang, X Zheng, S Xie, J Feng… - arXiv preprint arXiv …, 2024 - arxiv.org
The integration of multimodal Electronic Health Records (EHR) data has notably advanced
clinical predictive capabilities. However, current models that utilize clinical notes and …
相关文章 所有 2 个版本
[PDF] arxiv.org

SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction

Z Yu, X Chu, Y Jin, Y Wang, J Zhao - arXiv preprint arXiv:2405.09039, 2024 - arxiv.org
Electronic health record (EHR) data has emerged as a valuable resource for analyzing
patient health status. However, the prevalence of missing data in EHR poses significant …
相关文章 所有 2 个版本
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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs

M Jia, J Duan, Y Song, J Wang - arXiv preprint arXiv:2406.14326, 2024 - arxiv.org
Electronic Medical Records (EMRs), while integral to modern healthcare, present
challenges for clinical reasoning and diagnosis due to their complexity and information …
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