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Sequence Labeling for Disambiguating Medical Abbreviations

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Abstract

Abbreviations are unavoidable yet critical parts of the medical text. Using abbreviations, especially in clinical patient notes, can save time and space, protect sensitive information, and help avoid repetitions. However, most abbreviations might have multiple senses, and the lack of a standardized mapping system makes disambiguating abbreviations a difficult and time-consuming task. The main objective of this study is to examine the feasibility of sequence labeling methods for medical abbreviation disambiguation. Specifically, we explore the capability of sequence labeling methods to deal with multiple unique abbreviations in a single text. We use two public datasets to compare and contrast the performance of several transformer models pre-trained on different scientific and medical corpora. Our proposed sequence labeling approach outperforms the more commonly used text classification models for the abbreviation disambiguation task. In particular, the SciBERT model shows a strong performance for both sequence labeling and text classification tasks over the two considered datasets. Furthermore, we find that abbreviation disambiguation performance for the text classification models becomes comparable to that of sequence labeling only when postprocessing is applied to their predictions, which involves filtering possible labels for an abbreviation based on the training data.

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Data Availability

All the datasets used in our analysis are publicly available, and the links to these datasets are provided as follows: \(\bullet \) MeDAL, https://www.kaggle.com/datasets/xhlulu/medal-emnlp; \(\bullet \) UMN, https://conservancy.umn.edu/handle/11299/137703

Notes

  1. https://huggingface.co/

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Author information

Authors and Affiliations

  1. Toronto Metropolitan University, 44 Gerrard St E, Toronto, M5B 1G3, Ontario, Canada

    Mucahit Cevik, Sanaz Mohammad Jafari, Mitchell Myers & Savas Yildirim

  2. Faculty of Engineering and Natural Sciences, Istanbul Bilgi Univeristy, Eyüpsultan, 34060, İstanbul, Turkey

    Savas Yildirim

Authors
  1. Mucahit Cevik
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  4. Savas Yildirim
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Contributions

All the co-authors contributed to the conception, design, implementation, writing, and review of the paper. Author order is alphabetical.

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Correspondence to Mucahit Cevik.

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The authors declare no competing interests.

Appendix. Text classification postprocessing results

Appendix. Text classification postprocessing results

In this section, we have reported the detailed results for the text classification experiments in Sect. 4.2. Table 9 presents the performance values before and after applying postprocessing. Overall, we observe that almost all the models benefit from postprocessing. In particular, DistilBERT, BlueBERT, and MS-BERT experience a significant performance improvement for the MeDAL dataset. On the other hand, BioBERT and SciBERT models’ performances do not benefit from the postprocessing approach on the UMN dataset.

Table 9 Summary performance values for transformers-based text classification models for the full-label datasets with and without postprocessing. Results are averaged over 3 folds (highest scores on each metric are in bold)
Full size table

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Cevik, M., Mohammad Jafari, S., Myers, M. et al. Sequence Labeling for Disambiguating Medical Abbreviations. J Healthc Inform Res 7, 501–526 (2023). https://doi.org/10.1007/s41666-023-00146-1

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