Health Information Processing
Springer Nature Switzerland AG (Verlag)
978-981-96-3751-5 (ISBN)
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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.
.- 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.
.- Construction of a Traditional Chinese Medicine stroke knowledge graph and its inferential diagnosis integrated with Large Language Models
.- 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.
| Erscheinungsdatum | 14.05.2025 |
|---|---|
| Reihe/Serie | Communications in Computer and Information Science |
| Zusatzinfo | 68 Illustrations, color; 15 Illustrations, black and white |
| Verlagsort | Cham |
| Sprache | englisch |
| Original-Titel | Health Information Processing |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Informatik ► Weitere Themen ► Bioinformatik | |
| Medizin / Pharmazie | |
| Technik ► Maschinenbau | |
| Technik ► Medizintechnik | |
| Schlagworte | Applications of multimodal learning in medical areas • Case Reports • Clinical Research Data • Computational health information analysis • Cross modality learning • Deep learning for health and medicine • Knowledge sources for medical knowledge engineering • Large Model • Machine learning algorithms for health information processing • Medical Basic Model • Multi-modality medical data processing • Next-generation health information processing technologies • patient self-reported data • pre-trained model • Social Media • Using novel data sources • Using novel data sources for clinical decision making |
| ISBN-10 | 981-96-3751-1 / 9819637511 |
| ISBN-13 | 978-981-96-3751-5 / 9789819637515 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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