Papers › MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding...
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
Zhi Wen, Xing Han Lu, Siva Reddy
One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Mortality Prediction | MIMIC-III | ELECTRA (pretrained) | Accuracy | 0.8443 | #9 of 13 | Archive leaderboard | report |
| Mortality Prediction | MIMIC-III | ELECTRA (from scratch) | Accuracy | 0.8325 | #10 of 13 | Archive leaderboard | report |
| Mortality Prediction | MIMIC-III | LSTM+SA (pretrained) | Accuracy | 0.8298 | #11 of 13 | Archive leaderboard | report |
| Mortality Prediction | MIMIC-III | LSTM (pretrained) | Accuracy | 0.828 | #12 of 13 | Archive leaderboard | report |
| Mortality Prediction | MIMIC-III | LSTM+SA (from scratch) | Accuracy | 0.7996 | #13 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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