# MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

arXiv: https://arxiv.org/abs/2012.13978 · HTML: https://mlsanity.com/paper/2012.13978/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 18 of 18)

- [Lossless data compression by large models](https://mlsanity.com/paper/2407.07723/) (2024)
- [Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks](https://mlsanity.com/paper/2406.12066/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Fine-Tuning Transformers: Vocabulary Transfer](https://mlsanity.com/paper/2112.14569/) (2021)
- [UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?](https://mlsanity.com/paper/2306.16931/) (2023)
- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education](https://mlsanity.com/paper/2403.08272/) (2024)
- [MedINST: Meta Dataset of Biomedical Instructions](https://mlsanity.com/paper/2410.13458/) (2024)
- [Uncertainty-aware abstention in medical diagnosis based on medical texts](https://mlsanity.com/paper/2502.18050/) (2025)
- [GLADIS: A General and Large Acronym Disambiguation Benchmark](https://mlsanity.com/paper/2302.01860/) (2023)
- [Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field](https://mlsanity.com/paper/2407.14076/) (2024)
- [Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia](https://mlsanity.com/paper/2204.12169/) (2022)
- [Improving Privacy Benefits of Redaction](https://mlsanity.com/paper/2501.17762/) (2025)
- [Token Classification for Disambiguating Medical Abbreviations](https://mlsanity.com/paper/2210.02487/) (2022)
- [Echoes of Biases: How Stigmatizing Language Affects AI Performance](https://mlsanity.com/paper/2305.10201/) (2023)
- [SPOT: Text Source Prediction from Originality Score Thresholding](https://mlsanity.com/paper/2405.20505/) (2024)
- [Towards Quantifying The Privacy Of Redacted Text](https://mlsanity.com/paper/2410.07772/) (2024)
- [PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation](https://mlsanity.com/paper/2603.23678/) (2026)

## Abstract (arXiv)

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.