Health Information Processing
portes grátis
Health Information Processing
10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 15-17, 2024, Proceedings, Part II
Lin, Hongfei; Liao, Xiangwen; Huang, Zhengxing; Liu, Lei; Tang, Buzhou; Hao, Tianyong; Chen, Qingcai; Zhang, Yanchun
Springer Nature Switzerland AG
05/2025
272
Mole
Inglês
9789819637515
Pré-lançamento - envio 15 a 20 dias após a sua edição
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.- 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.
.- 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Medical Basic Model;Large Model;Multi-modality medical data processing;Cross modality learning;Applications of multimodal learning in medical areas;Knowledge sources for medical knowledge engineering;Machine learning algorithms for health information processing;Pre-trained Model;Computational health information analysis;Deep learning for health and medicine;Next-generation health information processing technologies;Using novel data sources ;patient self-reported data;case reports;social media;clinical research data;Using novel data sources for clinical decision making
.- 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.
.- 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Medical Basic Model;Large Model;Multi-modality medical data processing;Cross modality learning;Applications of multimodal learning in medical areas;Knowledge sources for medical knowledge engineering;Machine learning algorithms for health information processing;Pre-trained Model;Computational health information analysis;Deep learning for health and medicine;Next-generation health information processing technologies;Using novel data sources ;patient self-reported data;case reports;social media;clinical research data;Using novel data sources for clinical decision making
