Enhancing Data Quality and Semantic Annotation by Combining Medical Ontology and Machine Learning Techniques

Zina Nakhla, Manel Sliti

2025

Abstract

Effective management of electronic health records (EHR) is a major challenge in the modern healthcare sector. Despite technological advances, the interoperability of medical data remains a crucial challenge. This complex problem is manifested by the diversity of data formats, the presence of multiple standards and the heterogeneity of Information Technology (IT) systems used in health- care establishments. However, the diversity of IT systems and the complexity of medical terminologies often make data interoperability and semantic annotation in the healthcare domain difficult. To address this challenge, our study proposes an innovative approach to standardize the representation of medical concepts, to automate the detection of medical abbreviations and to improve the contextual understanding of medical terms. We developed an ontological model to harmonize the representation of medical data, thus facilitating their exchange and integration between different health systems. In parallel, we used advanced machine learning techniques for automatic detection of medical abbreviations in medical texts, and applied Natural Language Processing to improve contextual understanding of medical terms. The results of our study demonstrate the effectiveness of our approach in solving challenges related to medical data management. By combining different advanced techniques, our approach helps overcome barriers to medical data interoperability and paves the way for better healthcare system integration and improved patient care.

Paper Citation

in Harvard Style

Nakhla Z. and Sliti M. (2025). Enhancing Data Quality and Semantic Annotation by Combining Medical Ontology and Machine Learning Techniques. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-769-6, SciTePress, pages 140-150. DOI: 10.5220/0013750200004000

in Bibtex Style

@conference{keod25,
author={Zina Nakhla and Manel Sliti},
title={Enhancing Data Quality and Semantic Annotation by Combining Medical Ontology and Machine Learning Techniques},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2025},
pages={140-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013750200004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Enhancing Data Quality and Semantic Annotation by Combining Medical Ontology and Machine Learning Techniques
SN - 978-989-758-769-6
AU - Nakhla Z.
AU - Sliti M.
PY - 2025
SP - 140
EP - 150
DO - 10.5220/0013750200004000
PB - SciTePress