# Extracting Adverse Drug Events from Clinical Notes

arXiv: https://arxiv.org/abs/2104.10791 · HTML: https://mlsanity.com/paper/2104.10791/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)

## Built on by (most cited first, 7 of 7)

- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [Lightweight Transformers for Clinical Natural Language Processing](https://mlsanity.com/paper/2302.04725/) (2023)
- [Clinical Relation Extraction Using Transformer-based Models](https://mlsanity.com/paper/2107.08957/) (2021)
- [From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine](https://mlsanity.com/paper/2502.09242/) (2025)
- [Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings](https://mlsanity.com/paper/2506.14900/) (2025)
- [Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature](https://mlsanity.com/paper/2405.18605/) (2024)
- [Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models](https://mlsanity.com/paper/2504.03051/) (2025)

## Abstract (arXiv)

Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, dosage), the reason why the drug was initially prescribed, and any adverse reaction to the drug. This paper explores the relationship between a drug and its associated attributes using relation extraction techniques. We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our experimental results demonstrate that the contextualized language model-based approach outperformed other models overall and obtain the state-of-the-art performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an F1 score of 0.94; however, for certain relation types, the rule-based approach obtained a higher Precision and Recall than either learning approach.