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Full Description
This book CCIS 2884 constitutes the refereed proceedings of the 11th China Health Information Processing Conference, CHIP 2025, held in Dongguan, China, during November 22-24, 2025.
The 37 full papers included in this book were carefully reviewed and selected from 66 submissions. These papers have been categorized into 3 main topics: Biomedical data processing and model application, Mental health and disease prediction, and Drug prediction and Knowledge map.
Contents
Biomedical data processing and model application.- Breast-Rehab: A Postoperative Breast Cancer Rehabilitation Training Assessment System Based on Human Action Recognition.- KD4FIRE: A Knowledge Distillation Approach for Fine-grained Medical Relation Extraction in Low-Resource Settings.- Zero-Shot Knowledge Distillation for Chinese Clinical Diagnosis: Enhancing Small LLMs via Prompting and Loss-based Filtering.- CausalMPT: Causal Multimodal Prompt Tuning for Healthcare MIE.- Enabling AI Scientists to Recognize Innovation: A Domain-Agnostic Algorithm for Assessing Novelty.- Iterative Dynamic Routing Framework for Medical Question Answering.- CDMFuse: A Multi-Modal Fusion Framework for Skin Lesion Classification.- DMSNet: Dual-Channel Interactive Attention Deep Classification Network for Mass Spectrometry Data.- Composite Inflammatory Indices from Peripheral Blood Tests for Early Prediction of EBV-Associated He-mophagocytic Lymphohistiocytosis in Children.- Knowledge-Augmented Multimodal Learning for Breast Cancer Diagnosis.- Retrieval-Augmented Relation Extraction for Medical Knowledge Graphs.- A Verification-Enhanced Large Model Framework for Diabetes Knowledge Graph Construction.- A Deep Learning-Based TCM Deficiency-Excess Syndrome Differentiation Framework for Spectrum Analysis of PPG Pulse Wave.- Cross-Type Biomedical Named Entity Recognition Method Based on Knowledge Distillation.- Meta-Learning Enhance the Influenza Surveillance across Spatio-temporal Heterogeneous Scenario by Recommending suitable Statistical Models.- Construction of the Text Sentiment Analysis Model for College Students' Mental Health.- A Review of Multimodal Large-Model-Driven Intelligent Tongue, Pulse, and Facial Diagnosis in Traditional Chinese Medicine.- Domain-Adapted Large Language Models for Schema-Consistent Medical Record Generation from Doctor-Patient Dialogues.- ECU-BRE: NLI-based Biomedical Relation Extraction with EC Supervision and Uncertainty-aware Inference.- Application of Computer Vision and Deep Learning in Medical Imaging.- Magic-OR: A Multi-dimensional Geometric Alignment Framework for Precise Occlusal Registration Using Intraoral Scans.- Mental health and disease prediction.- Breast-Rehab: A Postoperative Breast Cancer Rehabilitation.- A Multi-Modal Fusion Framework for Skin Lesion Classification.- Composite Inflammatory Indices from Peripheral Blood Tests for Early Prediction of EBV-Associated He-mophagocytic Lymphohistiocytosis in Children.- A Verification-Enhanced Large Model Framework for Diabetes.- Meta-Learning Enhance the Influenza Surveillance across Spatio-temporal Heterogeneous Scenario by Recommending suitable Statistical Models.- Construction of the Text Sentiment Analysis Model for College Students' Mental Health.- A Review of Multimodal Large-Model-Driven Intelligent Tongue, Pulse, and Facial Diagnosis in Traditional Chinese Medicine.- Drug prediction and Knowledge map.- KD4FIRE: A Knowledge Distillation Approach for Fine-grained Medical Relation Extraction in Low-Resource Settings.- Zero-Shot Knowledge Distillation for Chinese Clinical Diagnosis: Enhancing Small LLMs via Prompting and Loss-based Filtering.- Enabling AI Scientists to Recognize Innovation: A Domain-Agnostic Algorithm for Assessing Novelty.- Knowledge-Augmented Multimodal Learning for Breast Cancer Diagnosis.- Retrieval-Augmented Relation Extraction for Medical Knowledge Graphs.- Cross-Type Biomedical Named Entity Recognition Method Based on Knowledge Distillation.- Shared task 1.- Overview of the Content Quality Control Task for Admission Records in Inpatient Electronic Medical Records in CHIP 2025.- Privacy-Preserving EMR QC with Rule Sharding and Multi-Agent Collaboration.- Dual Enhancement with In-Context Learning and Chain-of- Thought: Large Language Model-Driven Intelligent Connotation Quality Control of Medical Records.- Semantic Quallty Control of EMR Admission Notes:Integrating Rule Guidance, Prompt Optimization, and RAG.- Leveraging Phased Training and Multi-Granularity Prompting with Large Language Models for Few-Shot Quality Control of Electronic Medical Records.- M3-MedQC: A Method for Inherent Quality Control of Electronic Medical Records Based on Large Language Models and Multi-Granularity Evaluation.- Quality Control of Electronic Medical Records Content Based on Q-LoRA FIne-tuning and a Hybrid Model-Rule Approach.- Shared task 2.- Overview of CHIP 2025 Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records.- Towards Discharge Medication Recommendation via Multi-Scale Model Training and Multi-Dimensional Feature Enhancement.- DP-EMR: A Chinese Medication Recommendation Methodfor Metabolic Diseases based on Two-stage Ensemble Learning.- LoRA-Fine-Tuned LLMs for Discharge Medication Recommendation on Chinese EHRs.- Multi-Format Fine-Tuning and Optimized Voting Ensemble for Robust Medication Recommendation in Chinese EMRs.- Shared task 3.- Overview of Medical NLP Code Generation with FHIR for Clinical Trial Screening.- A Large Language Model-based System or Automatic Medical NLP Code Generation.- An Iterative Code Generation and Optimization Framework Based on Dynamic Few-Shot Learning for Medical Information Processing.- Prompt-Driven Program Synthesis for Clinical Trial Screening Criteria: From Natural Language to Executable FHIR Code Generation.
