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Titlebook: Health Information Processing; 9th China Health Inf Hua Xu,Qingcai Chen,Zhengxing Huang Conference proceedings 2024 The Editor(s) (if appli

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51#
發(fā)表于 2025-3-30 08:44:31 | 只看該作者
Biomedical Causal Relation Extraction Incorporated with External Knowledgeies, semantic relations and function type. In recent years, some related works have largely improved the performance of biomedical causal relation extraction. However, they only focus on contextual information and ignore external knowledge. In view of this, we introduce entity information from exter
52#
發(fā)表于 2025-3-30 13:35:43 | 只看該作者
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發(fā)表于 2025-3-30 18:07:17 | 只看該作者
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發(fā)表于 2025-3-31 00:30:32 | 只看該作者
Chapter-Level Stepwise Temporal Relation Extraction Based on?Event Information for?Chinese Clinical of many intelligent researches in the medical field. Most of the existing studies on temporal relation extraction remains at sentence-level tasks, however, the rich medical information and large number of specialized vocabularies in Chinese clinical medical texts lead to the fact that short clinica
55#
發(fā)表于 2025-3-31 03:30:07 | 只看該作者
56#
發(fā)表于 2025-3-31 06:41:47 | 只看該作者
Biomedical Event Detection Based on Dependency Analysis and Graph Convolution Networkdrug development. The existing methods treat event detection tasks as multi-classification or sequence annotation tasks, only considering the sequence representation of sentences and striving to obtain more contextual information in sequence models. However, they overlook the shortcomings of sequenc
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發(fā)表于 2025-3-31 11:26:34 | 只看該作者
58#
發(fā)表于 2025-3-31 15:10:00 | 只看該作者
Privacy-Preserving Medical Dialogue Generation Based on?Federated Learning in privacy-sensitive domains like healthcare, concerns related to legal regulations and data security continue to pose challenges, resulting in data silos as a major barrier to building secure medical dialogue generation models. Federated learning is a distributed model training approach that allow
59#
發(fā)表于 2025-3-31 19:50:59 | 只看該作者
FgKF: Fine-Grained Knowledge Fusion for Radiology Report Generationeration of image-to-report can effectively relieve pressure on physicians. The generation of radiology reports utilizes the terminology and expertise inherent to the field of radiology. The integration of this specialized knowledge into automated report generation not only enhances the precision of
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