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This article investigates the extraction of medical keywords from French-language gastroenterology records, manually written by healthcare professionals at the Department of Gastroenterology, Hôtel Dieu de France University Medical Center in Beirut, and their association with ICD10 codes to enhance medical technology. Using Named Entity Recognition (NER) techniques and the DR BERT-CASM2 model in conjunction with the Medkit library in Python, we aimed to automate the processes of extraction and association from the French clinical data. We achieved high accuracy, yielding promising results and offering a structured and standardized approach to medical record management. This study lays the foundation for future advancements in automated medical record analysis, paving the way for more efficient and accurate diagnosis and treatment, particularly in French-speaking medical environments.

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AI-Driven Automation of Medical Keyword Extraction and ICD10 Code Association in Gastroenterology Records

  • 会议论文

  • 发表日期：

    2025年

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  • Said El Khoury,
  • Carl Angelo Mikael,
  • Santa El Helou,
  • Tina Yaacoub,
  • Elio Mikhael,
  • Youssef Abou Boutros,
  • Ali Mansour,
  • Cesar Yaghi

摘要

This article investigates the extraction of medical keywords from French-language gastroenterology records, manually written by healthcare professionals at the Department of Gastroenterology, Hôtel Dieu de France University Medical Center in Beirut, and their association with ICD10 codes to enhance medical technology. Using Named Entity Recognition (NER) techniques and the DR BERT-CASM2 model in conjunction with the Medkit library in Python, we aimed to automate the processes of extraction and association from the French clinical data. We achieved high accuracy, yielding promising results and offering a structured and standardized approach to medical record management. This study lays the foundation for future advancements in automated medical record analysis, paving the way for more efficient and accurate diagnosis and treatment, particularly in French-speaking medical environments.