Zobrazeno 1 - 10
of 212
pro vyhledávání: '"Wang, Yasha"'
While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data. The integration of external knowledge from
Externí odkaz:
http://arxiv.org/abs/2408.13073
Autor:
Jiang, Xinke, Fang, Yue, Qiu, Rihong, Zhang, Haoyu, Xu, Yongxin, Chen, Hao, Zhang, Wentao, Zhang, Ruizhe, Fang, Yuchen, Chu, Xu, Zhao, Junfeng, Wang, Yasha
In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized
Externí odkaz:
http://arxiv.org/abs/2408.09199
Autor:
Zhang, Ruizhe, Xu, Yongxin, Xiao, Yuzhen, Zhu, Runchuan, Jiang, Xinke, Chu, Xu, Zhao, Junfeng, Wang, Yasha
By integrating external knowledge, Retrieval-Augmented Generation (RAG) has become an effective strategy for mitigating the hallucination problems that large language models (LLMs) encounter when dealing with knowledge-intensive tasks. However, in th
Externí odkaz:
http://arxiv.org/abs/2408.03297
Autor:
Zhu, Yinghao, Gao, Junyi, Wang, Zixiang, Liao, Weibin, Zheng, Xiaochen, Liang, Lifang, Wang, Yasha, Pan, Chengwei, Harrison, Ewen M., Ma, Liantao
The use of Large Language Models (LLMs) in medicine is growing, but their ability to handle both structured Electronic Health Record (EHR) data and unstructured clinical notes is not well-studied. This study benchmarks various models, including GPT-b
Externí odkaz:
http://arxiv.org/abs/2407.18525
Autor:
Wang, Tianlong, Jiao, Xianfeng, He, Yifan, Chen, Zhongzhi, Zhu, Yinghao, Chu, Xu, Gao, Junyi, Wang, Yasha, Ma, Liantao
Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to express fully and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring t
Externí odkaz:
http://arxiv.org/abs/2406.00034
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 challenges to existing methods, leading to spurious correlations and suboptim
Externí odkaz:
http://arxiv.org/abs/2405.09039
Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks. However, fine-tuning LoRA-series models also faces the risk of overfitting on the training dataset, a
Externí odkaz:
http://arxiv.org/abs/2404.09610
UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuit
Externí odkaz:
http://arxiv.org/abs/2403.05246
Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks to directly model
Externí odkaz:
http://arxiv.org/abs/2401.16796
Autor:
Zhang, Ruizhe, Jiang, Xinke, Fang, Yuchen, Luo, Jiayuan, Xu, Yongxin, Zhu, Yichen, Chu, Xu, Zhao, Junfeng, Wang, Yasha
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years. However, their performance is often constrained by a limited receptive
Externí odkaz:
http://arxiv.org/abs/2401.09943