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

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.04.2025

Herausgeber

Yanchun Zhang + weitere

Verlag

Springer Singapore

Seitenzahl

286

Maße (L/B/H)

23,5/15,5/1,7 cm

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9637-51-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.04.2025

Herausgeber

Verlag

Springer Singapore

Seitenzahl

286

Maße (L/B/H)

23,5/15,5/1,7 cm

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9637-51-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: GPSR Kontakt

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  • Produktbild: Health Information Processing
  • Produktbild: Health Information Processing
  • .- 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.