Health Information Processing

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.