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Record Nr. |
UNINA9910831500303321 |
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Autore |
Xu Hua |
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Titolo |
Health Information Processing [[electronic resource] ] : 9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, October 27–29, 2023, Proceedings / / edited by Hua Xu, Qingcai Chen, Hongfei Lin, Fei Wu, Lei Liu, Buzhou Tang, Tianyong Hao, Zhengxing Huang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (444 pages) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 1993 |
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Altri autori (Persone) |
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ChenQingcai |
LinHongfei |
WuFei |
LiuLei |
TangBuzhou |
HaoTianyong |
HuangZhengxing |
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Disciplina |
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Soggetti |
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Medical informatics |
Artificial intelligence |
Image processing - Digital techniques |
Computer vision |
Application software |
Information storage and retrieval systems |
Health Informatics |
Artificial Intelligence |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Computer and Information Systems Applications |
Information Storage and Retrieval |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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TIG-KIGNN: Time Interval Guided Knowledge Inductive Graph Neural Network for misinformation detection from Social Media -- A Bert |
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based relation extraction method with inter-entity constraints for Chinese EHRs -- Automatic Generation of Discharge Summary of EMRs Based on Multi-granularity Information Fusion -- A BART-based Study of Entity-Relationship Extraction for Electronic Medical Records of Cardiovascular Diseases -- Multilevel Asynchronous Time Network for Medication Recommendation -- Biomedical Event Detection of Based on Dependency Analysis and Graph Convolution Network -- Multi-head Attention and Graph Convolutional Networks with Regularized Dropout for Biomedical Relation Extraction -- Privacy-preserving Medical Dialogue Generation Based on Federated Learning -- Cross-Lingual Name Entity Recognition from Clinical Text using Mixed Language Query -- PEMRC: A Positive Enhanced Machine Reading Comprehension Method for Few-Shot Named Entity Recognition in Biomedical Domain -- Research on Double-Graphs Knowledge-Enhanced Intelligent Diagnosis -- FgKF: Fine-grained Knowledge Fusion for Radiology Report Generation -- Medical Entity recognition with few-shot based on Chinese character radicals -- Biomedical causal relation extraction incorporated with external knowledge -- Research on structured lung cancer electronic medical records based on BART joint extraction -- Biomedical Named Entity Recognition Based on Multi-task Learning -- Biomedical Relation Extraction via Syntax-Enhanced Contrastive Networks -- Entity Fusion Contrastive Inference Network for Biomedical Document Relation Extraction -- An Unsupervised Clinical Acronym Disambiguation Method based on Pretrained Language Model -- Combining Biaffine Model and Constraints Inference for Chinese Clinical Temporal Relation Extraction -- Automatic Prediction of Multiple Associated Diseases Using A Dual-attention Neural Network Model -- Chapter-level Stepwise Temporal Relation Extraction Based on Event Information for Chinese Clinical Medical Texts -- Constructing a Multi-scale Medical Knowledge Graph from Electronic Medical Records -- Double Graph Convolution Network with Knowledge Distillation for International Media Portrait Analysis of COVID-19 -- A Simple but Useful Multi-corpus Transferring Method for Biomedical Named Entity Recognition -- Time Series Prediction Models for Assisting the Diagnosis and Treatment of Gouty Arthritis -- Asymptomatic carriers are associated with shorter negative conversion time in children with Omicron infections. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 9th China Health Information Processing Conference, CHIP 2023, held in Hangzhou, China, during October 27–29, 2023. The 27 full papers included in this book were carefully reviewed and selected from 66 submissions. They were organized in topical sections as follows: healthcare information extraction; healthcare natural language processing; healthcare data mining and applications. |
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