Health Information Processing (eBook, PDF)

10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 15-17, 2024, Proceedings, Part II
Redaktion: Zhang, Yanchun; Huang, Zhengxing; Hao, Tianyong; Tang, Buzhou; Liao, Xiangwen; Liu, Lei; Lin, Hongfei; Chen, Qingcai

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 286
• Erscheinungstermin: 10. April 2025
• Englisch
• ISBN-13: 9789819637522
• Artikelnr.: 73854592

Inhaltsangabe

.- 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.