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CC BY 4.0 · Methods Inf Med
DOI: 10.1055/a-2521-4372
Original Article

Deciphering Abbreviations in Malaysian Clinical Notes Using Machine Learning

Ismat Mohd Sulaiman
1   Health Informatics Centre, Planning Division, Ministry of Health Malaysia, Putrajaya, Malaysia
,
Awang Bulgiba 
2   Academy of Sciences, Kuala Lumpur, Malaysia
,
Sameem Abdul Kareem
3   Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Wilayah Persekutuan, Malaysia
,
Abdul Aziz Latip
4   MIMOS Berhad, Kuala Lumpur, Malaysia
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Abstract

Objective This is the first Malaysian machine learning model to detect and disambiguate abbreviations in clinical notes. The model has been designed to be incorporated into MyHarmony, a natural language processing system, that extracts clinical information for health care management. The model utilizes word embedding to ensure feasibility of use, not in real-time but for secondary analysis, within the constraints of low-resource settings.

Methods A Malaysian clinical embedding, based on Word2Vec model, was developed using 29,895 electronic discharge summaries. The embedding was compared against conventional rule-based and FastText embedding on two tasks: abbreviation detection and abbreviation disambiguation. Machine learning classifiers were applied to assess performance.

Results The Malaysian clinical word embedding contained 7 million word tokens, 24,352 unique vocabularies, and 100 dimensions. For abbreviation detection, the Decision Tree classifier augmented with the Malaysian clinical embedding showed the best performance (F-score of 0.9519). For abbreviation disambiguation, the classifier with the Malaysian clinical embedding had the best performance for most of the abbreviations (F-score of 0.9903).

Conclusion Despite having a smaller vocabulary and dimension, our local clinical word embedding performed better than the larger nonclinical FastText embedding. Word embedding with simple machine learning algorithms can decipher abbreviations well. It also requires lower computational resources and is suitable for implementation in low-resource settings such as Malaysia. The integration of this model into MyHarmony will improve recognition of clinical terms, thus improving the information generated for monitoring Malaysian health care services and policymaking.

Keywords

electronic health record - discharge summaries - word embedding - machine learning - natural language processing - health system management


Publikationsverlauf

Eingereicht: 30. August 2024

Angenommen: 15. Januar 2025

Accepted Manuscript online:
22. Januar 2025

Artikel online veröffentlicht:
11. Februar 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

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