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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.
List 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.
