TY - GEN
T1 - AI-Driven Automation of Medical Keyword Extraction and ICD10 Code Association in Gastroenterology Records
AU - El Khoury, Said
AU - Mikael, Carl Angelo
AU - El Helou, Santa
AU - Yaacoub, Tina
AU - Mikhael, Elio
AU - Boutros, Youssef Abou
AU - Mansour, Ali
AU - Yaghi, Cesar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105000823068
U2 - 10.1007/978-3-031-84100-2_9
DO - 10.1007/978-3-031-84100-2_9
M3 - Conference contribution
AN - SCOPUS:105000823068
SN - 9783031840999
T3 - Lecture Notes in Electrical Engineering
SP - 77
EP - 83
BT - Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024
A2 - Ruo Roch, Massimo
A2 - Bellotti, Francesco
A2 - Berta, Riccardo
A2 - Martina, Maurizio
A2 - Motto Ros, Paolo
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024
Y2 - 19 September 2024 through 20 September 2024
ER -