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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Ali, Azliza Mohd
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Computer Science & IT

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Review of NLP in EMR: abbreviation, diagnosis, and ICD classification Iqbal Basheer, Nurul Anis Balqis; Nordin, Sharifalillah; Kasim, Sazzli Shahlan; Ali, Azliza Mohd; Abdul Hamid, Nurzeatul Hamimah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp881-891

Abstract

This review explores state-of-the-art natural language processing (NLP) methods applied to electronic medical records (EMRs) for key tasks such as expanding medical abbreviations, automated diagnosis generation, international classification of diseases (ICD) classification, and explaining model outcomes. With the growing digitization of healthcare data, the complexity of EMR analysis presents a significant challenge for accurate and interpretable results. This paper evaluates various methodologies, highlighting their strengths, limitations, and potential for improving clinical decision-making. Special attention is given to abbreviation expansion as a crucial step for disambiguating terms in the clinical text, followed by an exploration of auto-diagnosis models and ICD code assignment techniques. Finally, interpretability methods like integrated gradients and attention-based approaches are reviewed to understand model predictions and their applicability in healthcare. This review aims to provide a comprehensive guide for researchers and practitioners interested in leveraging NLP for clinical text analysis.
Co-Authors Abdul Hamid, Nurzeatul Hamimah Iqbal Basheer, Nurul Anis Balqis Kasim, Sazzli Shahlan Nordin, Sharifalillah