Health Information Processing. Evaluation Track Papers
Description
This book constitutes the refereed proceedings of the 11th China Health Information Processing Conference, CHIP 2025, held in Dongguan, China, November 22-24, 2025.
The 16 full papers included in this book were carefully reviewed and selected from 16 submissions. These papers focus on following topical sections: Shared task 1: Content Quality Control Task for Admission Records in Inpatient Electronic Medical Records; Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records; Shared Task 3: Medical NLP Code Generation with FHIR for Clinical Trial Screening.
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Content
.- Shared task 1: Content Quality Control Task for Admission Records in Inpatient Electronic Medical Records
.- Overview of the Content Quality Control Task for Admission Records in Inpatient Electronic Medical Records in CHIP 2025.
.- MedShard: Privacy-Preserving EMR QC with Rule Sharding and Multi-Agent Collaboration.
.- Dual Enhancement with In-Context Learning and Chain-ofThought: Large Language Model-Driven Intelligent Connotation Quality Control of Medical Records.
.- Semantic Quality 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: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records
.- 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 Method for 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: Medical NLP Code Generation with FHIR for Clinical Trial Screening
.- Overview of Medical NLP Code Generation with FHIR for Clinical Trial Screening.
.- A Large Language Model-based System for 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.