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Related Concept Videos

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Updated: May 24, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

MAP-CARE: Enhancing Cross-Lingual Medical Intervention Terms.

Hugo Guillen-Ramirez1, Karen Triep2, Christophe Gaudet-Blavignac3

  • 1Department of Visceral Surgery and Medicine, University Hospital, Bern, Switzerland.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MAP-CARE, a framework using Large Language Models (LLMs) to integrate multilingual medical procedure data. It enhances cross-lingual retrieval and interoperability across diverse healthcare systems.

Keywords:
Large Language Model (LLM)classification systemprocedure classificationsemantic embeddingterminology mapping

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Published on: December 6, 2024

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Healthcare Data Integration

Background:

  • Current systems struggle with cross-lingual medical procedure data alignment.
  • Non-English healthcare systems present significant data integration challenges.

Purpose of the Study:

  • To develop a pipeline for cross-lingual retrieval and integration of medical procedures data.
  • To enhance interoperability across languages and healthcare systems.

Main Methods:

  • Developed MAP-CARE, a novel framework leveraging Large Language Models (LLMs).
  • Utilized semantic embeddings for translating and transforming medical procedures into a unified multilingual space.

Main Results:

  • MAP-CARE demonstrated high accuracy in translating and mapping clinical terms.
  • Achieved up to 90% accuracy in cross-language translation of procedure codes across English, German, French, and Italian.

Conclusions:

  • MAP-CARE provides a flexible, scalable, and robust solution.
  • Enables multilingual and cross-system integration of medical procedural data.