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Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, October 2729, 2023, Proceedings 1st ed. 2024 [Pehme köide]

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  • Formaat: Paperback / softback, 432 pages, kõrgus x laius: 235x155 mm, kaal: 682 g, 102 Illustrations, color; 28 Illustrations, black and white; XV, 432 p. 130 illus., 102 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1993
  • Ilmumisaeg: 02-Feb-2024
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9819998638
  • ISBN-13: 9789819998630
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Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, October 2729, 2023, Proceedings 1st ed. 2024Suurem pilt
  • Formaat: Paperback / softback, 432 pages, kõrgus x laius: 235x155 mm, kaal: 682 g, 102 Illustrations, color; 28 Illustrations, black and white; XV, 432 p. 130 illus., 102 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1993
  • Ilmumisaeg: 02-Feb-2024
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9819998638
  • ISBN-13: 9789819998630
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789819998630.html
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.
TIG-KIGNN: Time Interval Guided Knowledge Inductive Graph Neural Network
for misinformation detection from Social Media.- A Bert 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.