Health Information Processing: 10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 15-17, 2024, Proceedings, Part II

by Yanchun Zhang (Editor), Qingcai Chen (Editor), Hongfei Lin (Editor), Lei Liu (Editor), Xiangwen Liao (Editor)

Overview

This two-volume set CCIS 2432-2433 constitutes the refereed proceedings of the 10th China Health Information Processing Conference, CHIP 2024, held in Fuzhou, China, during November 15–17, 2024.

The 32 full papers included in this set were carefully reviewed and selected from 65 submissions.

They are organized in topical sections as follows: biomedical data processing and model application; mental health and disease prediction; and drug prediction and knowledge map.

Product Details

ISBN-13:	9789819637515
Publisher:	Springer Nature Singapore
Publication date:	04/11/2025
Series:	Communications in Computer and Information Science , #2433
Pages:	286
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

.- Mental health and disease prediction.

.- Data Augmentation and Instruction Fine-Tuning for ADR Detection.

.- Deep Fusion Network with Feature Engineering for Discharge Risk Assessment.

.- Analysis of Risk Factors for Hemorrhagic Complications in Pediatric Acute Liver Failure.

.- PMFNet: Pseudo-modal fusion network for obstructive sleep apnea detection using single-lead ECG signals.

.- VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early Detection.

.- RAG Combined with Instruction Tuning for Traditional Chinese Medicine Syndrome Differentiation Thinking.

.- Drug prediction and Knowledge map.

.- MBF-DTI: A fused multi-dimensional biochemical feature-based drug target prediction method based on heterogeneous graph attention networks.

.- Structure and pseudo-ligand based drug discovery for disease targets.

.- Multi-channel hypergraph convolutional network predicts circRNA-drug sensitivity associations.

.- Knowledge Infusion Framework with LLMs for Few-Shot Biomedical Relation Extraction.

.- A review of drug-target interaction prediction methods.

.- The Joint Entity-Relation Extraction Model Based on Span and Interactive Fusion Representation for Chinese Medical Texts with Complex Semantics.

.- Multi-task learning-based knowledge graph question answering for pediatric epilepsy.

.- Hypertension Medication Recommendation Based on Synergy and Selectivity of Heterogeneous Medical Entities.

.- Integrating TCM's "One Root of Medicine and Food" Principle into Dietary Recommendations with Retrieval-Augmented LLMs.

.- OAGLLM: A Retrieval-Augmented Large Language Model for Medication Instructions.