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Produktbild: Health Information Processing. Evaluation Track Papers
Band 2917 Vorbesteller

Health Information Processing. Evaluation Track Papers 11th China Health Information Processing Conference, CHIP 2025, Dongguan, China, November 22–24, 2025, Proceedings

Aus der Reihe Communications in Computer and Information Science

Fr. 106.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.10.2026

Abbildungen

XV, 214 p. 42 illus., 29 illus. in color.

Herausgeber

Buzhou Tang + weitere

Verlag

Springer Singapore

Seitenzahl

214

Maße (L/B)

23.5/15.5 cm

Sprache

Englisch

ISBN

978-981-9226-57-3

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.10.2026

Abbildungen

XV, 214 p. 42 illus., 29 illus. in color.

Herausgeber

Verlag

Springer Singapore

Seitenzahl

214

Maße (L/B)

23.5/15.5 cm

Sprache

Englisch

ISBN

978-981-9226-57-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Health Information Processing. Evaluation Track Papers

  • .- 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.